Standard back propagation, as with many gradient based optimization methods converges slowly as neural network training problems become larger and more complex. This paper describes the employment of two algorithms to accelerate the training procedure in an automatic human face recognition system. As compared to standard back propagation, the convergence rate is improved by up to 98% with only a minimal increase in the complexity of each iteration.