A facial expression recognition scheme is presented in this paper, based on features derived from the optical flow between two instances of a face in the same emotional state. A pre-processing step of isolating the human face from the background is first employed by means of face detection and registration. A spatio-temporal description of the expression is then obtained by evaluating the Radon transform of the motion vectors between the face in its neutral condition and at the ‘apex’ of the expression. A linear curve normalization scheme is proposed, achieving a translation, scaling and resolution invariant representation of the Radon curves. Finally, experimental results are presented, illustrating the performance of the proposed algorithm for expression classification using a correlation criterion and a neural network classifier.