diff --git a/test/test_minimization/test_minimizers.py b/test/test_minimization/test_minimizers.py
index 7320c7a40fbc74c8881401a1834ae5930aedc8f7..c3b84899e4d9e77930cfb21d82c4c532cf3952c9 100644
--- a/test/test_minimization/test_minimizers.py
+++ b/test/test_minimization/test_minimizers.py
@@ -29,6 +29,9 @@ minimizers = [ift.SteepestDescent, ift.RelaxedNewton, ift.VL_BFGS,
               ift.ConjugateGradient, ift.NonlinearCG,
               ift.NewtonCG, ift.L_BFGS_B]
 
+minimizers2 = [ift.RelaxedNewton, ift.VL_BFGS, ift.NonlinearCG,
+               ift.NewtonCG, ift.L_BFGS_B]
+
 
 class Test_Minimizers(unittest.TestCase):
 
@@ -57,5 +60,67 @@ class Test_Minimizers(unittest.TestCase):
                         1./covariance_diagonal.to_global_data(),
                         rtol=1e-3, atol=1e-3)
 
+    @expand(product(minimizers2))
+    def test_rosenbrock(self, minimizer_class):
+        try:
+            from scipy.optimize import rosen, rosen_der, rosen_hess_prod
+        except ImportError:
+            raise SkipTest
+        np.random.seed(42)
+        space = ift.UnstructuredDomain((2,))
+        starting_point = ift.Field.from_random('normal', domain=space)*10
+
+        class RBEnergy(ift.Energy):
+            def __init__(self, position):
+                super(RBEnergy, self).__init__(position)
+
+            @property
+            def value(self):
+                return rosen(self._position.to_global_data().copy())
+
+            @property
+            def gradient(self):
+                inp = self._position.to_global_data().copy()
+                out = ift.Field.from_global_data(space, rosen_der(inp))
+                return out
+
+            @property
+            def curvature(self):
+                class RBCurv(ift.EndomorphicOperator):
+                    def __init__(self, loc):
+                        self._loc = loc.to_global_data().copy()
+
+                    @property
+                    def domain(self):
+                        return space
+
+                    @property
+                    def capability(self):
+                        return self.TIMES
+
+                    def apply(self, x, mode):
+                        self._check_input(x, mode)
+                        inp = x.to_global_data().copy()
+                        out = ift.Field.from_global_data(
+                            space, rosen_hess_prod(self._loc.copy(), inp))
+                        return out
 
-# MR FIXME: add Rosenbrock test
+                t1 = ift.GradientNormController(tol_abs_gradnorm=1e-5,
+                                                iteration_limit=1000)
+                t2 = ift.ConjugateGradient(controller=t1)
+                return ift.InversionEnabler(RBCurv(self._position),
+                                            inverter=t2)
+
+        IC = ift.GradientNormController(tol_abs_gradnorm=1e-5,
+                                        iteration_limit=10000)
+        try:
+            minimizer = minimizer_class(controller=IC)
+            energy = RBEnergy(position=starting_point)
+
+            (energy, convergence) = minimizer(energy)
+        except NotImplementedError:
+            raise SkipTest
+
+        assert_equal(convergence, IC.CONVERGED)
+        assert_allclose(energy.position.to_global_data(), 1.,
+                        rtol=1e-3, atol=1e-3)