diff --git a/stable_baselines/common/policies.py b/stable_baselines/common/policies.py
index a2f3cc7f1c..93bdd57120 100644
--- a/stable_baselines/common/policies.py
+++ b/stable_baselines/common/policies.py
@@ -435,11 +435,13 @@ def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm=256
         else:  # Use the new net_arch parameter
             if layers is not None:
                 warnings.warn("The new net_arch parameter overrides the deprecated layers parameter.")
-            if feature_extraction == "cnn":
-                raise NotImplementedError()
 
             with tf.variable_scope("model", reuse=reuse):
-                latent = tf.layers.flatten(self.processed_obs)
+                if feature_extraction == "cnn":
+                    latent = cnn_extractor(self.processed_obs, **kwargs)
+                    latent = tf.layers.flatten(latent)
+                else:
+                    latent = tf.layers.flatten(self.processed_obs)
                 policy_only_layers = []  # Layer sizes of the network that only belongs to the policy network
                 value_only_layers = []  # Layer sizes of the network that only belongs to the value network
 
diff --git a/tests/test_cnn_lstm_policy.py b/tests/test_cnn_lstm_policy.py
new file mode 100644
index 0000000000..58cfef3ca3
--- /dev/null
+++ b/tests/test_cnn_lstm_policy.py
@@ -0,0 +1,71 @@
+import os
+
+import numpy as np
+import tensorflow as tf
+import pytest
+from gym import make
+from gym.wrappers.time_limit import TimeLimit
+
+from stable_baselines.ppo2 import PPO2
+from stable_baselines.common.policies import CnnLstmPolicy
+from stable_baselines.common.evaluation import evaluate_policy
+from stable_baselines.common.tf_layers import conv, linear, conv_to_fc
+
+
+def custom_cnn_extractor(input_images):
+    activ = tf.nn.relu
+    layer_1 = activ(conv(input_images, 'c1', n_filters=8, filter_size=3, stride=1, init_scale=np.sqrt(2)))
+    layer_2 = activ(conv(layer_1, 'c2', n_filters=8, filter_size=3, stride=1, init_scale=np.sqrt(2)))
+    layer_2 = conv_to_fc(layer_2)
+    return activ(linear(layer_2, 'fc1', n_hidden=256, init_scale=np.sqrt(2)))
+
+
+class CustomCnnLstmPolicy1(CnnLstmPolicy):
+    def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm=32, reuse=False, **_kwargs):
+        super().__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm, reuse,
+                         cnn_extractor=custom_cnn_extractor, **_kwargs)
+
+
+class CustomCnnLstmPolicy2(CnnLstmPolicy):
+    def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm=32, reuse=False, **_kwargs):
+        super().__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm, reuse,
+                         net_arch=['lstm', 8], cnn_extractor=custom_cnn_extractor, **_kwargs)
+
+
+class CustomCnnLstmPolicy3(CnnLstmPolicy):
+    def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm=32, reuse=False, **_kwargs):
+        super().__init__(sess, ob_space, ac_space, n_env, n_steps, n_batch, n_lstm, reuse,
+                         net_arch=[8, 'lstm', dict(vf=[5, 10], pi=[10])],
+                         cnn_extractor=custom_cnn_extractor, **_kwargs)
+
+
+POLICIES = [CnnLstmPolicy, CustomCnnLstmPolicy1, CustomCnnLstmPolicy2, CustomCnnLstmPolicy3]
+
+
+def make_env(i):
+    env = make("Breakout-v0")
+    env = TimeLimit(env, max_episode_steps=20)
+    env.seed(i)
+    return env
+
+
+@pytest.mark.parametrize("policy", POLICIES)
+@pytest.mark.expensive
+def test_cnn_lstm_policy(request, policy):
+    model_fname = './test_model_{}.zip'.format(request.node.name)
+
+    try:
+        env = make_env(0)
+        model = PPO2(policy, env, nminibatches=1)
+        model.learn(total_timesteps=15)
+        env = model.get_env()
+        evaluate_policy(model, env, n_eval_episodes=5)
+        # saving
+        model.save(model_fname)
+        del model, env
+        # loading
+        _ = PPO2.load(model_fname, policy=policy)
+
+    finally:
+        if os.path.exists(model_fname):
+            os.remove(model_fname)