There has been an increasing interest in the applicability of neural networks in disparate domains. In this paper, we describe the use of multi-layered perceptrons, a type of neural-network topology, for financial classification problems, with promising results. Back-propagation, which is the learning algorithm most often used in multi-layered perceptrons, however, is inherently an inefficient search procedure. We present improved procedures which have much better convergence properties. Using several financial classification applications as examples, we show the efficacy of using multilayered perceptrons with improved learning algorithms. The modified learning algorithms have better performance, in terms of classification/prediction accuracies, than the methods previously used in the literature, such as probit analysis and similarity-based learning techniques.