The development and application of multivariate statisticaltechniques in process monitoring has gained substantial interestover the past two decades in academia and industry alike. Initially developed for monitoring and fault diagnosis in complexsystems, such techniques have been refined and applied in variousengineering areas, for example mechanical and manufacturing,chemical, electrical and electronic, and power engineering. The recipe for the tremendous interest in multivariate statisticaltechniques lies in its simplicity and adaptability for developingmonitoring applications. In contrast, competitive model,signal or knowledge based techniques showed their potential onlywhenever cost-benefit economics have justified the required effortin developing applications. Statistical Monitoring of Complex Multivariate Processespresents recent advances in statistics based process monitoring,explaining how these processes can now be used in areas such asmechanical and manufacturing engineering for example, in additionto the traditional chemical industry. This book: Contains a detailed theoretical background of the componenttechnology. Brings together a large body of work to address thefield’s drawbacks, and develops methods for theirimprovement. Details cross-disciplinary utilization, exemplified by examplesin chemical, mechanical and manufacturing engineering. Presents real life industrial applications, outliningdeficiencies in the methodology and how to address them. Includes numerous examples, tutorial questions and homeworkassignments in the form of individual and team-based projects, toenhance the learning experience. Features a supplementary website including Matlab algorithmsand data sets. This book provides a timely reference text to the rapidlyevolving area of multivariate statistical analysis for academics,advanced level students, and practitioners alike.