diff --git a/scikits/learn/tests/test_pipeline.py b/scikits/learn/tests/test_pipeline.py
index edec37420c6492d11523f8c9a93d1309bee88d01..f846a585677c6f20ff777559b738d5626cd5f8f6 100644
--- a/scikits/learn/tests/test_pipeline.py
+++ b/scikits/learn/tests/test_pipeline.py
@@ -4,22 +4,31 @@ Test the pipeline module.
 
 from nose.tools import assert_raises, assert_equal, assert_false
 
+import numpy as np
+
 from ..base import BaseEstimator, clone
 from ..pipeline import Pipeline
 from ..svm import SVC
+from ..glm import LogisticRegression
 from ..feature_selection import SelectKBest, f_classif
+from ..datasets import load_iris
+
 
 class IncorrectT(BaseEstimator):
     """Small class to test parameter dispatching.
     """
+
     def __init__(self, a=None, b=None):
         self.a = a
         self.b = b
 
+
 class T(IncorrectT):
+
     def fit(self, X, y):
         return self
 
+
 def test_pipeline_init():
     """ Test the various init parameters of the pipeline.
     """
@@ -67,3 +76,24 @@ def test_pipeline_init():
     params2.pop('svc')
     params2.pop('anova')
     assert_equal(params, params2)
+
+
+def test_pipeline_methods():
+    """ Test the various methods of the pipeline.
+    """
+    iris = load_iris()
+    X = iris.data
+    y = iris.target
+    # Test with Anova+SVC
+    clf = LogisticRegression()
+    filter1 = SelectKBest(f_classif, k=2)
+    pipe = Pipeline([('anova', filter1), ('svc', clf)])
+    pipe.fit(X, y)
+    pipe.predict(X)
+    pipe.predict_proba(X)
+    pipe.predict_log_proba(X)
+    pipe.score(X, y)
+    support_ = pipe.get_support()
+    assert np.sum(support_) == 2
+    coef_ = pipe.coef_
+    assert np.size(coef_) == 4