Neural model for the recognition of transitive actions The visual recognition of goal-directed movements is crucial for imitation and possibly the understanding of actions.We present a neurophysiologically-inspired model for the recognition of goal-directed hand movements. The model exploits neural principles that have been used before to account for object and action recognition: (i) hierarchical neural architecture extracting form and motion features; (ii) optimization of mid-level features by learning; (iii) realization of temporal sequence selectivity by recurrent neural circuits. Beyond these classical principles, the model proposes novel physiologically plausible mechanisms for the integration of information about effector shape, motion, goal object, and affordance. We demonstrate that the model is powerful enough to recognize hand actions from real video sequences and reproduces charac- teristic properties of real cortical neurons involved in action recognition. We conclude that: (i) goal-directed actions can be recognized by view-based mechanisms without a simulation of the actions in 3-D, (ii) well-established neural principles of object and motion recognition are sufficient to account for the visual recognition of goal-directed transitive actions.