Artificial neural networks have been defined to be intricate systems of simple units which adapt to their environments, and offer a numerical based approach to processing data, similar to data intensive statistical methods, and in contrast to the more traditional (symbolic) programmed systems.

Artificial neural networks comprise a number of interconnected processing elements (nodes) which are analogous to a biological neuron. Numerical weights are associated with the links (connections) between the nodes, which, essentially, constitute the knowledge of the system. These weights are derived during a period of training, where the system is repeatedly presented with a set of examples until some predetermined convergence criterion is achieved. Hence the system is deemed to learn automatically. They are currently being applied to a wide range of problem domains, including stock market forecasting, mortgage underwriting, and food manufacture.