Artificial neural networks are generally described as nonparametric; that is, the use of a neural network does not require any assumptions about the statistical distribution of the data. The performance of a neural network depends to a significant extent on how well it has been trained, and not on the adequacy of assumptions concerning the statistical distribution of the data, as is the case with the maximum likelihood classifier. During the training phase, the neural network “learns” about regularities present in the training data and, based on these regularities, constructs rules that can be extended to the unknown data. This is a special ability of neural networks. However, the user must determine the architecture of the network, and also define parameters such as the learning rate, which affect the training time, performance, and the rate of convergence of a neural network. There are no clear rules to assist with the design of the network, and only rules of thumb (or heuristics) exist to guide users in their choice of network parameters.