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Object recognition and especially object class recognition is and will be a key capability in home robotics when robots have to tackle manipulation tasks and grasp new objects or just have to search for objects. The goal is to have a robot classify 'never before seen objects' at first occurrence in a single view in a fast and robust manner. The classification task can be seen as a matching problem, finding the most appropriate 3D model and view with respect to a given depth image. We introduce a single-view shape model based classification approach using RGB-D sensors and a novel matching procedure for depth image to 3D model matching leading inherently to object classification. Utilizing the inter-view similarity of the 3D models for enhanced matching, the average precision of our descriptors is increased of up to 15% resulting in high classification accuracy. The presented adaptation of 3D shape descriptors to 2.5D data enables us to calculate the features in real time, directly from the 3D points of the sensor, without any calculation of normals or generating a mesh from it which is typical of state-of-art methods. Furthermore, we introduce a semi-automatic, user-centric approach to utilize the Internet for acquiring the required training data in the form of 3D models which significantly reduces the time for teaching new categories.