An edge orientation-based algorithm for multi-view object recognition is presented in this paper. The distribution of edge point orientations, combined with the normalized second moments, is taken as a feature vector to describe and index each object instance. For each unknown testing object, a set of likelihood weights for all the possible candidate objects is obtained by computing the Euclidean distances between the unknown feature set and all the available template feature vectors. A convincing coefficient is introduced to evaluate the confidence of the best match. New views (photo shots) will be automatically taken if the best match is thought to be insufficiently convincing. In the experiments, our algorithm has achieved an average of 91.5% correct recognition rate under the 5-view scheme for 320 testing images taken from eight natural objects.