The availability of advanced information technologies such as the various types of automatic data acquisitions and sensor systems has created a tremendous capability to collect valuable process data. The effective and timely processing of this data is the backbone of intelligent process monitoring and control. The need for more practical process monitoring models continues to grow as these technologies become more sophisticated. This book presents effective methods for processing sensor data in order to achieve intelligent process monitoring and control strategies. In developing these methods, the following issues were considered: (a) how to represent secondary data in such a way that the features conserve the condition information essential for real-time decisions; (b) how to obtain a set of parsimonious features that are able to capture new information and also preserve the condition information in the original data; and (c) how to reduce the uncertainty in selecting subset features for prediction purposes. The book is an indispensable resource for researchers, professionals, and graduate students in industrial engineering, manufacturing, and related fields of study.