Face recognition has been a challenging task in academic research and in industrial applications. Defined by many distinct features (and the relative spatial position of those features), faces are very complex targets. Small inter-facial differences with high intra-facial variability makes face recognition a particularly complex task. Until now, face recognition systems have relied on input from the visual domain. The neural network system reported in this paper recognizes faces based on sonar input. Research on echolocating bats has demonstrated that bats can use sonar to accurately perceive detailed descriptions of objects. Previous research has shown that a sonar neural network system can recognize simple, 3D targets regardless of orientation (Dror, et al., 1995). In the present study we examine the effectiveness of using sonar input for more complex target recongition tasks. We use sonar echoes from faces (recorded in a variety of facial expressions) to train a neural network to recognize faces, regardless of facial expressions. After training, we examine the network's ability to generalize and correctly recognize the faces based on echoes from novel facial expressions, which were not included in the training set. The performance of the network on these novel echoes was 100% correct. To insure that our results were not due to the specific faces we used, we replicated our results two more times using different faces. Again, performance was almost perfect--99.6% and 100%. The results show that a neural network can learn to recognize faces based on sonar, and demonstrate that sonar can be a very effective input for neural networks that perform pattern recognition tasks.