A face recognition system has been developed and demonstrated at the Rutgers University Center for Computer Aids for Industrial Productivity. The system uses a preliminary data reduction step. gray scale projections, and a fast transform technique to greatly reduce the computational complexity of the problem and, consequently, the cost of high-speed implementation. The decision function is a few, extremely cost-effective neural network, the Mammone/Sankar Neural Tree Network. This network can be trained and re-trained rapidly on face image data and the system has built-in facilities for acquiring and editing a large data base of face images. Recognition rates higher than 90% were achieved on data sets containing up to 269 subjects. More importantly, it performed well on subjects with and without their glasses, under a wide range of changes in facial expressions, and under a variety of small tilts, translations and rotations.