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This paper describes a novel approach to robotic haptic recognition, which exploits an unsupervised Kohonen self-organizing feature map for performing a match-to-sample classification of three-dimensional (3-D) objects. The results obtained, even though currently referring to a simulated environment and to some working assumptions, have emphasized the validity of the approach and its applicability in a variety of dextrous robotic systems. Index Terms - Haptic recognition, high-dexterity robotic hands, neural networks.