In recent years neural computing has emerged as a practical technology, with successful applications in many fields. The majority of these applications are concerned with problems in pattern recognition, and make use of feed-forward network architectures such as the multi-layer perceptron and the radial basis function network. Also, it has also become widely acknowledged that successful applications of neural computing require a principled, rather than ad hoc, approach. My aim in writing this book has been to provide a more focused treatment of neural networks than previously available, which reflects these developments. By deliberately concentrating on the pattern recognition aspects of neural networks, it has become possible to treat many important topics in much greater depth. For example, density estimation, error functions, parameter optimization algorithms, data pre-processing, and Bayesian methods are each the subject of an entire chapter.