diff --git a/nolearn/lasagne/base.py b/nolearn/lasagne/base.py
index fd54fe1..f794b1c 100644
--- a/nolearn/lasagne/base.py
+++ b/nolearn/lasagne/base.py
@@ -2,6 +2,7 @@
 
 from .._compat import pickle
 from collections import OrderedDict
+from difflib import SequenceMatcher
 import functools
 import itertools
 import operator
@@ -395,28 +396,55 @@ def get_all_params(self):
         params = sum([l.get_params() for l in layers], [])
         return unique(params)
 
-    def load_weights_from(self, source):
-        self.initialize()
-
-        if isinstance(source, str):
-            source = np.load(source)
-
-        if isinstance(source, NeuralNet):
-            source = source.get_all_params()
-
-        source_weights = [
-            w.get_value() if hasattr(w, 'get_value') else w for w in source]
-
-        for w1, w2 in zip(source_weights, self.get_all_params()):
-            if w1.shape != w2.get_value().shape:
-                continue
-            w2.set_value(w1)
-
     def save_weights_to(self, fname):
         weights = [w.get_value() for w in self.get_all_params()]
         with open(fname, 'wb') as f:
             pickle.dump(weights, f, -1)
 
+    @staticmethod
+    def _param_alignment(shapes0, shapes1):
+        shapes0 = list(map(str, shapes0))
+        shapes1 = list(map(str, shapes1))
+        matcher = SequenceMatcher(a=shapes0, b=shapes1)
+        matches = []
+        for block in matcher.get_matching_blocks():
+            if block.size == 0:
+                continue
+            matches.append((list(range(block.a, block.a + block.size)),
+                            list(range(block.b, block.b + block.size))))
+        result = [line for match in matches for line in zip(*match)]
+        return result
+
+    def load_weights_from(self, src):
+        if not hasattr(self, '_initialized'):
+            raise AttributeError(
+                "Please initialize the net before loading weights using "
+                "the '.initialize()' method.")
+
+        if isinstance(src, str):
+            src = np.load(src)
+        if isinstance(src, NeuralNet):
+            src = src.get_all_params()
+
+        target = self.get_all_params()
+        src_params = [p.get_value() if hasattr(p, 'get_value') else p
+                      for p in src]
+        target_params = [p.get_value() for p in target]
+
+        src_shapes = [p.shape for p in src_params]
+        target_shapes = [p.shape for p in target_params]
+        matches = self._param_alignment(src_shapes, target_shapes)
+
+        for i, j in matches:
+            target[j].set_value(src_params[i])
+
+            if not self.verbose:
+                continue
+            param_shape = 'x'.join(map(str, src_params[i].shape))
+            param_name = target[j].name + ' ' if target[j].name else None
+            print("* Loaded parameter {}(shape: {})".format(
+                param_name, param_shape))
+
     def __getstate__(self):
         state = dict(self.__dict__)
         for attr in (
diff --git a/nolearn/tests/test_lasagne.py b/nolearn/tests/test_lasagne.py
index ca1283b..f42660d 100644
--- a/nolearn/tests/test_lasagne.py
+++ b/nolearn/tests/test_lasagne.py
@@ -115,10 +115,61 @@ def on_epoch_finished(nn, train_history):
 
     # Use load_weights_from to initialize an untrained model:
     nn3 = clone(nn_def)
+    nn3.initialize()
     nn3.load_weights_from(nn2)
     assert np.array_equal(nn3.predict(X_test), y_pred)
 
 
+def test_lasagne_loading_params_matches():
+    # Loading mechanism should find layers with matching parameter
+    # shapes, even if they are not perfectly aligned.
+    from nolearn.lasagne import NeuralNet
+
+    layers0 = [('input', InputLayer),
+               ('dense0', DenseLayer),
+               ('dense1', DenseLayer),
+               ('output', DenseLayer)]
+    net0 = NeuralNet(
+        layers=layers0,
+        input_shape=(None, 784),
+        dense0_num_units=100,
+        dense1_num_units=200,
+        output_nonlinearity=softmax, output_num_units=10,
+        update=nesterov_momentum,
+        update_learning_rate=0.01,
+        max_epochs=5,
+        )
+    net0.initialize()
+    net0.save_weights_to('tmp_params.np')
+
+    layers1 = [('input', InputLayer),
+               ('dense0', DenseLayer),
+               ('dense1', DenseLayer),
+               ('dense2', DenseLayer),
+               ('output', DenseLayer)]
+    net1 = NeuralNet(
+        layers=layers1,
+        input_shape=(None, 784),
+        dense0_num_units=100,
+        dense1_num_units=20,
+        dense2_num_units=200,
+        output_nonlinearity=softmax, output_num_units=10,
+        update=nesterov_momentum,
+        update_learning_rate=0.01,
+        max_epochs=5,
+        )
+    net1.initialize()
+
+    # output weights have the same shape but should differ
+    assert not (net0.layers_['output'].W.get_value() ==
+                net1.layers_['output'].W.get_value()).all()
+    # after loading, these weights should be equal, despite the
+    # additional dense layer
+    net1.load_weights_from('tmp_params.np')
+    assert (net0.layers_['output'].W.get_value() ==
+            net1.layers_['output'].W.get_value()).all()
+
+
 def test_lasagne_functional_grid_search(mnist, monkeypatch):
     # Make sure that we can satisfy the grid search interface.
     from nolearn.lasagne import NeuralNet