Pattern recognition has a long and respectable history within engineering, especially for military applications, but the cost of the hardware both to acquire the data (signals and images) and to compute the answers made it for many years a rather specialist subject. Hardware advances have made the concerns of pattern recognition of much wider applicability. In essence it covers the following problem:

There are many examples from everyday life:

Many of these are currently performed by human experts, but it is increasingly becoming feasible to design automated systems to replace the expert and either perform better (as in credit scoring) or ‘clone’ the expert (as in aids to medical diagnosis).

Neural networks have arisen from analogies with models of the way that humans might approach pattern recognition tasks, although they have developed a long way from the biological roots. Great claims have been made for these procedures, and although few of these claims have withstood careful scrutiny, neural network methods have had great impact on pattern recognition practice.