This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example). So that readers can develop their skills and understanding, many of the real data sets used in the book are available from the author's website: www.stats.ox.ac.uk/~ripley/PRbook/. For the same reason, many examples are included to illustrate real problems in pattern recognition. Unifying principles are highlighted, and the author gives an overview of the state of the subject, making the book valuable to experienced researchers in statistics, machine learning/artificial intelligence and engineering. The clear writing style means that the book is also a superb introduction for non-specialists.

‘The combination of theory and examples makes this a unique and interesting book.’

A. Gelman Source: Journal of the International Statistical Institute

‘I can warmly recommend this book. Every researcher will benefit by the broadness of Ripley’s view and the comprehensive bibliography.’

Dee Denteneer Source: ITW Nieuws

‘… a grand overview of both the theory and the practice of the field … of benefit to anyone who has an interest in a principled approach to statistical data analysis … will indeed provide an excellent reference for many years to come.’

Stephen Roberts Source: The Times Higher Education Supplement

‘... an excellent text on the statistics of pattern classifiers and the application of neural network techniques … Ripley has managed … to produce an altogether accessible text …[it] will be rightly popular with newcomers to the area for its ability to present the mathematics of statistical pattern recognition and neural networks in an accessible format and engaging style.’

Source: Nature

‘… a valuable reference for engineers and science researchers.’

Source: Optics and Photonics News