diff --git a/training/mozilla_voice_stt_training/train.py b/training/mozilla_voice_stt_training/train.py
index 13c97aa8..8e9c73f7 100644
--- a/training/mozilla_voice_stt_training/train.py
+++ b/training/mozilla_voice_stt_training/train.py
@@ -74,7 +74,7 @@ def create_overlapping_windows(batch_x):
     return batch_x
 
 
-def dense(name, x, units, dropout_rate=None, relu=True):
+def dense(name, x, units, dropout_rate=None, relu=True, layer_norm=False):
     with tfv1.variable_scope(name):
         bias = variable_on_cpu('bias', [units], tfv1.zeros_initializer())
         weights = variable_on_cpu('weights', [x.shape[-1], units], tfv1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"))
@@ -84,6 +84,10 @@ def dense(name, x, units, dropout_rate=None, relu=True):
     if relu:
         output = tf.minimum(tf.nn.relu(output), FLAGS.relu_clip)
 
+    if layer_norm:
+        with tfv1.variable_scope(name):
+            output = tf.contrib.layers.layer_norm(output)
+
     if dropout_rate is not None:
         output = tf.nn.dropout(output, rate=dropout_rate)
 
@@ -177,9 +181,9 @@ def create_model(batch_x, seq_length, dropout, reuse=False, batch_size=None, pre
 
     # The next three blocks will pass `batch_x` through three hidden layers with
     # clipped RELU activation and dropout.
-    layers['layer_1'] = layer_1 = dense('layer_1', batch_x, Config.n_hidden_1, dropout_rate=dropout[0])
-    layers['layer_2'] = layer_2 = dense('layer_2', layer_1, Config.n_hidden_2, dropout_rate=dropout[1])
-    layers['layer_3'] = layer_3 = dense('layer_3', layer_2, Config.n_hidden_3, dropout_rate=dropout[2])
+    layers['layer_1'] = layer_1 = dense('layer_1', batch_x, Config.n_hidden_1, dropout_rate=dropout[0], layer_norm=FLAGS.layer_norm)
+    layers['layer_2'] = layer_2 = dense('layer_2', layer_1, Config.n_hidden_2, dropout_rate=dropout[1], layer_norm=FLAGS.layer_norm)
+    layers['layer_3'] = layer_3 = dense('layer_3', layer_2, Config.n_hidden_3, dropout_rate=dropout[2], layer_norm=FLAGS.layer_norm)
 
     # `layer_3` is now reshaped into `[n_steps, batch_size, 2*n_cell_dim]`,
     # as the LSTM RNN expects its input to be of shape `[max_time, batch_size, input_size]`.
@@ -196,7 +200,7 @@ def create_model(batch_x, seq_length, dropout, reuse=False, batch_size=None, pre
     layers['rnn_output_state'] = output_state
 
     # Now we feed `output` to the fifth hidden layer with clipped RELU activation
-    layers['layer_5'] = layer_5 = dense('layer_5', output, Config.n_hidden_5, dropout_rate=dropout[5])
+    layers['layer_5'] = layer_5 = dense('layer_5', output, Config.n_hidden_5, dropout_rate=dropout[5], layer_norm=FLAGS.layer_norm)
 
     # Now we apply a final linear layer creating `n_classes` dimensional vectors, the logits.
     layers['layer_6'] = layer_6 = dense('layer_6', layer_5, Config.n_hidden_6, relu=False)
diff --git a/training/mozilla_voice_stt_training/util/flags.py b/training/mozilla_voice_stt_training/util/flags.py
index e5ad8758..9185d326 100644
--- a/training/mozilla_voice_stt_training/util/flags.py
+++ b/training/mozilla_voice_stt_training/util/flags.py
@@ -135,6 +135,7 @@ def create_flags():
     # Geometry
 
     f.DEFINE_integer('n_hidden', 2048, 'layer width to use when initialising layers')
+    f.DEFINE_boolean('layer_norm', True, 'wether to use layer-normalization after each fully-connected layer (except the last one)')
 
     # Initialization