Correlation is a robust and general technique for pattern recognition and is used in many applications, such as automatic target recognition, biometric recognition and optical character recognition. The design, analysis and use of correlation pattern recognition algorithms requires background information, including linear systems theory, random variables and processes, matrix/vector methods, detection and estimation theory, digital signal processing and optical processing. This book provides a needed review of this diverse background material and develops the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises. It shows both digital and optical implementations. It also contains state-of-the-art technology presented by the team that developed it and includes case studies of significant current interest, such as face and fingerprint recognition. Suitable for advanced undergraduate or graduate students taking courses in pattern recognition theory, whilst reaching technical levels of interest to the professional practitioner.