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Approaches based on local features and descriptors are increasingly used for the task of object recognition due to their robustness with regard to occlusions and geometrical deformations of objects. In this paper we present a local feature based, rotation-invariant Neoperceptron. By extending the weight-sharing properties of convolutional neural networks to orientations, we obtain a neural network that is inherently robust to object rotations, while still being capable to learn optimally discriminant features from training data. The performance of the network is evaluated on a facial expression database and compared to a standard Neoperceptron as well as to the Scale Invariant Feature Transform (SIFT), a-state-of-the-art local descriptor. The results confirm the validity of our approach.