A support vector machine based algorithm for corner detection is presented. It is based on computing the direction of maximum gray-level change for each edge pixel in an image, and then representing the edge pixel by a four dimensional feature vector constituted by the count of other edge pixels lying in a window centred about and having each of the possible four directions as their direction of maximum local gray-level change. A support vector machine is designed using this feature vectors and the support vectors, representing critical points in a classification problem, correspond to the corner points. The algorithm is straightforward and does not involve computation of complex differential geometric operators. It has implicit learning capability resulting in good performance for a wide range of images.