diff --git a/REQUIRE b/REQUIRE
index 870ab04..81015dc 100644
--- a/REQUIRE
+++ b/REQUIRE
@@ -1,3 +1,4 @@
 julia 0.6
 IntervalSets 0.1
+IterTools
 RangeArrays
diff --git a/src/AxisArrays.jl b/src/AxisArrays.jl
index 90cfed1..2d023cb 100644
--- a/src/AxisArrays.jl
+++ b/src/AxisArrays.jl
@@ -3,9 +3,11 @@ __precompile__()
 module AxisArrays
 
 using Base: tail
+import Base.Iterators: repeated
 using RangeArrays, IntervalSets
+using IterTools
 
-export AxisArray, Axis, axisnames, axisvalues, axisdim, axes, atindex, atvalue
+export AxisArray, Axis, axisnames, axisvalues, axisdim, axes, atindex, atvalue, collapse
 
 # From IntervalSets:
 export ClosedInterval, ..
@@ -15,6 +17,7 @@ include("intervals.jl")
 include("search.jl")
 include("indexing.jl")
 include("sortedvector.jl")
+include("categoricalvector.jl")
 include("combine.jl")
 
 end
diff --git a/src/categoricalvector.jl b/src/categoricalvector.jl
new file mode 100644
index 0000000..e4a35ed
--- /dev/null
+++ b/src/categoricalvector.jl
@@ -0,0 +1,82 @@
+"""
+A CategoricalVector is an AbstractVector which is treated as a categorical axis regardless
+of the element type. Duplicate values are not allowed but are not filtered out.
+
+A CategoricalVector axis can be indexed with an ClosedInterval, with a value, or with a
+vector of values. Use of a CategoricalVector{Tuple} axis allows indexing similar to the
+hierarchical index of the Python Pandas package or the R data.table package.
+
+In general, indexing into a CategoricalVector will be much slower than the corresponding
+SortedVector or another sorted axis type, as linear search is required.
+
+### Constructors
+
+```julia
+CategoricalVector(x::AbstractVector)
+```
+
+### Arguments
+
+* `x::AbstractVector` : the wrapped vector
+
+### Examples
+
+```julia
+v = CategoricalVector(collect([1; 8; 10:15]))
+A = AxisArray(reshape(1:16, 8, 2), v, [:a, :b])
+A[Axis{:row}(1), :]
+A[Axis{:row}(10), :]
+A[Axis{:row}([1, 10]), :]
+
+## Hierarchical index example with three key levels
+
+data = reshape(1.:40., 20, 2)
+v = collect(zip([:a, :b, :c][rand(1:3,20)], [:x,:y][rand(1:2,20)], [:x,:y][rand(1:2,20)]))
+A = AxisArray(data, CategoricalVector(v), [:a, :b])
+A[:b, :]
+A[[:a,:c], :]
+A[(:a,:x), :]
+A[(:a,:x,:x), :]
+```
+"""
+immutable CategoricalVector{T, A<:AbstractVector{T}} <: AbstractVector{T}
+    data::A
+end
+
+function CategoricalVector(data::AbstractVector{T}) where T
+    CategoricalVector{T, typeof(data)}(data)
+end
+
+Base.getindex(v::CategoricalVector, idx::Int) = v.data[idx]
+Base.getindex(v::CategoricalVector, idx::AbstractVector) = CategoricalVector(v.data[idx])
+
+Base.length(v::CategoricalVector) = length(v.data)
+Base.size(v::CategoricalVector) = size(v.data)
+Base.size(v::CategoricalVector, i) = size(v.data, i)
+Base.indices(v::CategoricalVector) = indices(v.data)
+
+axistrait(::Type{CategoricalVector{T,A}}) where {T,A} = Categorical
+checkaxis(::CategoricalVector) = nothing
+
+
+## Add some special indexing for CategoricalVector{Tuple}'s to achieve something like
+## Panda's hierarchical indexing
+
+axisindexes{T<:Tuple,S,A}(ax::Axis{S,CategoricalVector{T,A}}, idx) = axisindexes(ax, (idx,))
+
+function axisindexes{T<:Tuple,S,A}(ax::Axis{S,CategoricalVector{T,A}}, idx::Tuple)
+    collect(filter(ax_idx->_tuple_matches(ax.val[ax_idx], idx), indices(ax.val)...))
+end
+
+function _tuple_matches(element::Tuple, idx::Tuple)
+    length(idx) <= length(element) || return false
+
+    for (x, y) in zip(element, idx)
+        x == y || return false
+    end
+
+    return true
+end
+
+axisindexes{T<:Tuple,S,A}(ax::Axis{S,CategoricalVector{T,A}}, idx::AbstractArray) =
+    vcat([axisindexes(ax, i) for i in idx]...)
diff --git a/src/combine.jl b/src/combine.jl
index 45f2907..3452d5c 100644
--- a/src/combine.jl
+++ b/src/combine.jl
@@ -139,3 +139,197 @@ function Base.join{T,N,D,Ax}(As::AxisArray{T,N,D,Ax}...; fillvalue::T=zero(T),
     return result
 
 end #join
+
+function _collapse_array_axes(array_name, array_axes...)
+    ((array_name, (idx isa Tuple ? idx : (idx,))...) for idx in product((Ax.val for Ax in array_axes)...))
+end
+
+function _collapse_axes(array_names, array_axes)
+    collect(Iterators.flatten(map(array_names, array_axes) do tup_name, tup_array_axes
+        _collapse_array_axes(tup_name, tup_array_axes...)
+    end))
+end
+
+function _splitall{N}(::Type{Val{N}}, As...)
+    tuple((Base.IteratorsMD.split(A, Val{N}) for A in As)...)
+end
+
+function _reshapeall{N}(::Type{Val{N}}, As...)
+    tuple((reshape(A, Val{N}) for A in As)...)
+end
+
+function _check_common_axes(common_axis_tuple)
+    if !all(axisname(first(common_axis_tuple)) .=== axisname.(common_axis_tuple[2:end]))
+        throw(ArgumentError("Leading common axes must have the same name in each array"))
+    end
+
+    return nothing
+end
+
+function _collapsed_axis_eltype(LType, trailing_axes)
+    eltypes = map(trailing_axes) do array_trailing_axes
+        Tuple{LType, eltype.(array_trailing_axes)...}
+    end
+
+    return typejoin(eltypes...)
+end
+
+function collapse{N, AN}(::Type{Val{N}}, As::Vararg{AxisArray, AN})
+    collapse(Val{N}, ntuple(identity, Val{AN}), As...)
+end
+
+function collapse{N, AN, NewArrayType<:AbstractArray}(::Type{Val{N}}, ::Type{NewArrayType}, As::Vararg{AxisArray, AN})
+    collapse(Val{N}, NewArrayType, ntuple(identity, Val{AN}), As...)
+end
+
+@generated function collapse{N, AN, LType}(::Type{Val{N}}, labels::NTuple{AN, LType}, As::Vararg{AxisArray, AN})
+    collapsed_dim_int = Int(N) + 1
+    new_eltype = Base.promote_eltype(As...)
+
+    quote
+        collapse(Val{N}, Array{$new_eltype, $collapsed_dim_int}, labels, As...)
+    end
+end
+
+"""
+    collapse(::Type{Val{N}}, As::AxisArray...) -> AxisArray
+    collapse(::Type{Val{N}}, labels::Tuple, As::AxisArray...) -> AxisArray
+    collapse(::Type{Val{N}}, ::Type{NewArrayType}, As::AxisArray...) -> AxisArray
+    collapse(::Type{Val{N}}, ::Type{NewArrayType}, labels::Tuple, As::AxisArray...) -> AxisArray
+
+Collapses `AxisArray`s with `N` equal leading axes into a single `AxisArray`.
+All additional axes in any of the arrays are collapsed into a single additional
+axis of type `Axis{:collapsed, CategoricalVector{Tuple}}`.
+
+### Arguments
+
+* `::Type{Val{N}}`: the greatest common dimension to share between all input
+                    arrays. The remaining axes are collapsed. All `N` axes must be common
+                    to each input array, at the same dimension. Values from `0` up to the
+                    minimum number of dimensions across all input arrays are allowed.
+* `labels::Tuple`: (optional) an index for each array in `As` used as the leading element in
+                   the index tuples in the `:collapsed` axis. Defaults to `1:length(As)`.
+* `::Type{NewArrayType<:AbstractArray{_, N+1}}`: (optional) the desired underlying array
+                                                 type for the returned `AxisArray`.
+* `As::AxisArray...`: `AxisArray`s to be collapsed together.
+
+### Examples
+
+```
+julia> price_data = AxisArray(rand(10), Axis{:time}(Date(2016,01,01):Day(1):Date(2016,01,10)))
+1-dimensional AxisArray{Float64,1,...} with axes:
+    :time, 2016-01-01:1 day:2016-01-10
+And data, a 10-element Array{Float64,1}:
+ 0.885014
+ 0.418562
+ 0.609344
+ 0.72221
+ 0.43656
+ 0.840304
+ 0.455337
+ 0.65954
+ 0.393801
+ 0.260207
+
+julia> size_data = AxisArray(rand(10,2), Axis{:time}(Date(2016,01,01):Day(1):Date(2016,01,10)), Axis{:measure}([:area, :volume]))
+2-dimensional AxisArray{Float64,2,...} with axes:
+    :time, 2016-01-01:1 day:2016-01-10
+    :measure, Symbol[:area, :volume]
+And data, a 10×2 Array{Float64,2}:
+ 0.159434     0.456992
+ 0.344521     0.374623
+ 0.522077     0.313256
+ 0.994697     0.320953
+ 0.95104      0.900526
+ 0.921854     0.729311
+ 0.000922581  0.148822
+ 0.449128     0.761714
+ 0.650277     0.135061
+ 0.688773     0.513845
+
+julia> collapsed = collapse(Val{1}, (:price, :size), price_data, size_data)
+2-dimensional AxisArray{Float64,2,...} with axes:
+    :time, 2016-01-01:1 day:2016-01-10
+    :collapsed, Tuple{Symbol,Vararg{Symbol,N} where N}[(:price,), (:size, :area), (:size, :volume)]
+And data, a 10×3 Array{Float64,2}:
+ 0.885014  0.159434     0.456992
+ 0.418562  0.344521     0.374623
+ 0.609344  0.522077     0.313256
+ 0.72221   0.994697     0.320953
+ 0.43656   0.95104      0.900526
+ 0.840304  0.921854     0.729311
+ 0.455337  0.000922581  0.148822
+ 0.65954   0.449128     0.761714
+ 0.393801  0.650277     0.135061
+ 0.260207  0.688773     0.513845
+
+julia> collapsed[Axis{:collapsed}(:size)] == size_data
+true
+```
+
+"""
+@generated function collapse(::Type{Val{N}},
+                             ::Type{NewArrayType},
+                             labels::NTuple{AN, LType},
+                             As::Vararg{AxisArray, AN}) where {N, AN, LType, NewArrayType<:AbstractArray}
+    if N < 0
+        throw(ArgumentError("collapse dimension N must be at least 0"))
+    end
+
+    if N > minimum(ndims.(As))
+        throw(ArgumentError(
+            """
+            collapse dimension N must not be greater than the maximum number of dimensions
+            across all input arrays
+            """
+        ))
+    end
+
+    collapsed_dim = Val{N + 1}
+    collapsed_dim_int = Int(N) + 1
+
+    common_axes, trailing_axes = zip(_splitall(Val{N}, axisparams.(As)...)...)
+
+    foreach(_check_common_axes, zip(common_axes...))
+
+    new_common_axes = first(common_axes)
+    collapsed_axis_eltype = _collapsed_axis_eltype(LType, trailing_axes)
+    collapsed_axis_type = CategoricalVector{collapsed_axis_eltype, Vector{collapsed_axis_eltype}}
+
+    new_axes_type = Tuple{new_common_axes..., Axis{:collapsed, collapsed_axis_type}}
+    new_eltype = Base.promote_eltype(As...)
+
+    quote
+        common_axes, trailing_axes = zip(_splitall(Val{N}, axes.(As)...)...)
+
+        for common_axis_tuple in zip(common_axes...)
+            if !isempty(common_axis_tuple)
+                for common_axis in common_axis_tuple[2:end]
+                    if !all(axisvalues(common_axis) .== axisvalues(common_axis_tuple[1]))
+                        throw(ArgumentError(
+                            """
+                            Leading common axes must be identical across
+                            all input arrays"""
+                        ))
+                    end
+                end
+            end
+        end
+
+        array_data = cat($collapsed_dim, _reshapeall($collapsed_dim, As...)...)
+
+        axis_array_type = AxisArray{
+            $new_eltype,
+            $collapsed_dim_int,
+            $NewArrayType,
+            $new_axes_type
+        }
+
+        new_axes = (
+            first(common_axes)...,
+            Axis{:collapsed, $collapsed_axis_type}($collapsed_axis_type(_collapse_axes(labels, trailing_axes))),
+        )
+
+        return axis_array_type(array_data, new_axes)
+    end
+end
diff --git a/src/core.jl b/src/core.jl
index 236bb80..d29847d 100644
--- a/src/core.jl
+++ b/src/core.jl
@@ -503,6 +503,15 @@ end
 axes(A::AbstractArray) = default_axes(A)
 axes(A::AbstractArray, dim::Int) = default_axes(A)[dim]
 
+"""
+    axisparams(::AxisArray) -> Vararg{::Type{Axis}}
+    axisparams(::Type{AxisArray}) -> Vararg{::Type{Axis}}
+
+Returns the axis parameters for an AxisArray.
+"""
+axisparams{T,N,D,Ax}(::AxisArray{T,N,D,Ax}) = (Ax.parameters...)
+axisparams{T,N,D,Ax}(::Type{AxisArray{T,N,D,Ax}}) = (Ax.parameters...)
+
 ### Axis traits ###
 abstract type AxisTrait end
 immutable Dimensional <: AxisTrait end
diff --git a/src/indexing.jl b/src/indexing.jl
index 42aff72..8d404ed 100644
--- a/src/indexing.jl
+++ b/src/indexing.jl
@@ -231,6 +231,17 @@ end
     ex = Expr(:tuple)
     n = 0
     for i=1:length(I)
+        if axistrait(I[i]) <: Categorical && i <= length(Ax.parameters)
+            if I[i] <: Axis
+                push!(ex.args, :(axisindexes(A.axes[$i], I[$i].val)))
+            else
+                push!(ex.args, :(axisindexes(A.axes[$i], I[$i])))
+            end
+            n += 1
+
+            continue
+        end
+
         if I[i] <: Idx
             push!(ex.args, :(I[$i]))
             n += 1
@@ -243,7 +254,11 @@ end
             end
             n += length(I[i])
         elseif i <= length(Ax.parameters)
-            push!(ex.args, :(axisindexes(A.axes[$i], I[$i])))
+            if I[i] <: Axis
+                push!(ex.args, :(axisindexes(A.axes[$i], I[$i].val)))
+            else
+                push!(ex.args, :(axisindexes(A.axes[$i], I[$i])))
+            end
             n += 1
         else
             push!(ex.args, :(error("dimension ", $i, " does not have an axis to index")))
diff --git a/test/categoricalvector.jl b/test/categoricalvector.jl
new file mode 100644
index 0000000..28cf6ce
--- /dev/null
+++ b/test/categoricalvector.jl
@@ -0,0 +1,21 @@
+# Test CategoricalVector with a hierarchical index (indexed using Tuples)
+srand(1234)
+data = reshape(1.:40., 20, 2)
+v = collect(zip([:a, :b, :c][rand(1:3,20)], [:x,:y][rand(1:2,20)], [:x,:y][rand(1:2,20)]))
+idx = sortperm(v)
+A = AxisArray(data[idx,:], AxisArrays.CategoricalVector(v[idx]), [:a, :b])
+@test A[:b, :] == A[5:12, :]
+@test A[[:a,:c], :] == A[[1:4;13:end], :]
+@test A[(:a,:y), :] == A[2:4, :]
+@test A[(:c,:y,:y), :] == A[16:end, :]
+@test AxisArrays.axistrait(axes(A)[1]) <: AxisArrays.Categorical
+
+v = AxisArrays.CategoricalVector(collect([1; 8; 10:15]))
+@test AxisArrays.axistrait(axes(A)[1]) <: AxisArrays.Categorical
+A = AxisArray(reshape(1:16, 8, 2), v, [:a, :b])
+@test A[Axis{:row}(AxisArrays.CategoricalVector([15]))] == AxisArray(reshape(A.data[8, :], 1, 2), AxisArrays.CategoricalVector([15]), [:a, :b])
+@test A[Axis{:row}(AxisArrays.CategoricalVector([15])), 1] == AxisArray([A.data[8, 1]], AxisArrays.CategoricalVector([15]))
+@test AxisArrays.axistrait(axes(A)[1]) <: AxisArrays.Categorical
+
+# TODO: maybe make this work? Would require removing or modifying Base.getindex(A::AxisArray, idxs::Idx...)
+# @test A[AxisArrays.CategoricalVector([15]), 1] == AxisArray([A.data[8, 1]], AxisArrays.CategoricalVector([15]))
diff --git a/test/combine.jl b/test/combine.jl
index b8548ef..2334a40 100644
--- a/test/combine.jl
+++ b/test/combine.jl
@@ -47,3 +47,29 @@ ABdata[3:6,3:6,:,2] = Bdata
 @test join(A,B,method=:left) == AxisArray(ABdata[1:4, 1:4, :, :], A.axes...)
 @test join(A,B,method=:right) == AxisArray(ABdata[3:6, 3:6, :, :], B.axes...)
 @test join(A,B,method=:outer) == join(A,B)
+
+# collapse
+A1 = AxisArray(A1data, Axis{:X}(1:2), Axis{:Y}(1:2))
+A2 = AxisArray(reshape(A2data, size(A2data)..., 1), Axis{:X}(1:2), Axis{:Y}(1:2), Axis{:Z}([:foo]))
+
+@test @inferred(collapse(Val{2}, A1, A2)) == AxisArray(cat(3, A1data, A2data), Axis{:X}(1:2), Axis{:Y}(1:2), Axis{:collapsed}(AxisArrays.CategoricalVector([(1,), (2, :foo)])))
+@test @inferred(collapse(Val{2}, A1)) == AxisArray(reshape(A1, 2, 2, 1), Axis{:X}(1:2), Axis{:Y}(1:2), Axis{:collapsed}(AxisArrays.CategoricalVector([(1,)])))
+@test @inferred(collapse(Val{2}, A1)) == AxisArray(reshape(A1.data, size(A1)..., 1), axes(A1)..., Axis{:collapsed}(AxisArrays.CategoricalVector([(1,)])))
+
+@test @inferred(collapse(Val{2}, (:A1, :A2), A1, A2)) == AxisArray(cat(3, A1data, A2data), Axis{:X}(1:2), Axis{:Y}(1:2), Axis{:collapsed}(AxisArrays.CategoricalVector([(:A1,), (:A2, :foo)])))
+@test @inferred(collapse(Val{2}, (:foo,), A1)) == AxisArray(reshape(A1, 2, 2, 1), Axis{:X}(1:2), Axis{:Y}(1:2), Axis{:collapsed}(AxisArrays.CategoricalVector([(:foo,)])))
+@test @inferred(collapse(Val{2}, (:a,), A1)) == AxisArray(reshape(A1.data, size(A1)..., 1), axes(A1)..., Axis{:collapsed}(AxisArrays.CategoricalVector([(:a,)])))
+
+@test @inferred(collapse(Val{0}, A1)) == AxisArray(vec(A1data), Axis{:collapsed}(AxisArrays.CategoricalVector(collect(IterTools.product((1,), axisvalues(A1)...)))))
+@test @inferred(collapse(Val{1}, A1)) == AxisArray(A1data, Axis{:row}(1:2), Axis{:collapsed}(AxisArrays.CategoricalVector(collect(IterTools.product((1,), axisvalues(A1)[2])))))
+@test @inferred(collapse(Val{1}, (1,), A1)) == collapse(Val{1}, A1)
+@test @inferred(collapse(Val{1}, Array{Int, 2}, A1)) == collapse(Val{1}, A1)
+@test @inferred(collapse(Val{1}, Array{Int, 2}, (1,), A1)) == collapse(Val{1}, A1)
+
+@test_throws ArgumentError collapse(Val{-1}, A1)
+@test_throws ArgumentError collapse(Val{10}, A1)
+
+A1ᵀ = transpose(A1)
+@test_throws ArgumentError collapse(Val{-1}, A1, A1ᵀ)
+@test_throws ArgumentError collapse(Val{1}, A1, A1ᵀ)
+@test_throws ArgumentError collapse(Val{10}, A1, A1ᵀ)
diff --git a/test/core.jl b/test/core.jl
index ecd192c..fa1d6f0 100644
--- a/test/core.jl
+++ b/test/core.jl
@@ -240,8 +240,10 @@ map!(*, A2, A, A)
 # Reductions (issue #55)
 A = AxisArray(collect(reshape(1:15,3,5)), :y, :x)
 B = @inferred(AxisArray(collect(reshape(1:15,3,5)), Axis{:y}(0.1:0.1:0.3), Axis{:x}(10:10:50)))
-for C in (A, B)
-    for op in (sum, minimum)  # together, cover both reduced_indices and reduced_indices0
+arrays = (A, B)
+functions = (sum, minimum)
+for C in arrays
+    for op in functions  # together, cover both reduced_indices and reduced_indices0
         axv = axisvalues(C)
         C1 = @inferred(op(C, 1))
         @test typeof(C1) == typeof(C)
diff --git a/test/runtests.jl b/test/runtests.jl
index 161c89d..9e09eb4 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -1,5 +1,6 @@
 using AxisArrays
 using Base.Test
+import IterTools
 
 @testset "AxisArrays" begin
     @test isempty(detect_ambiguities(AxisArrays, Base, Core))
@@ -20,6 +21,10 @@ using Base.Test
         include("sortedvector.jl")
     end
 
+    @testset "CategoricalVector" begin
+        include("categoricalvector.jl")
+    end
+
     @testset "Search" begin
         include("search.jl")
     end