The performance of industrial robots working in unstructured environment can be improved using visual perception and learning techniques. In this work, a novel approach that uses 2D data and simple image processing techniques is introduced. A unique image vector descriptor (CFD&POSE) containing also depth information is computed and then input to a FuzzyARTMAP architecture for learning and recognition purposes. This vector compresses 3D object data from assembly parts and is invariant to scale, rotation and orientation. The approach in combination with the fast learning capability of ART networks indicates the suitability for industrial robot applications as it is shown in experimental results.