Abstract: When human faces rotate in image depth, even the faces of the same person appear with great variances. In this paper, neural network ensemble is applied to view invariant face recognition. The facial features used are extracted through view specific eigenface analysis. Several neural networks are trained, each for an eigenspace of different views, and their results are combined with another neural network. After the ensemble is trained, view estimation is not required for recognition. Moreover, when new faces are fed, the ensemble will not only give the recognition result but also present an estimated view information. Experimental results show that the recognition accuracy of the proposed approach is better than that of the best individual neural network selected according to the information provided by an accurate front end view estimation process.