Lihui Wang is a professor of virtual manufacturing at the University of Skovde's Virtual Systems Research Centre in Sweden. He was previously a senior research scientist at the Integrated Manufacturing Technologies Institute, National Research Council of Canada. He is also an adjunct professor in the Department of Mechanical and Materials Engineering at the University of Western Ontario, and a registered professional engineer in Canada. His research interests and responsibilities are in web-based and sensor-driven real-time monitoring and control, distributed machining process planning, adaptive assembly planning, collaborative design, supply chain management, as well as intelligent and adaptive manufacturing systems. Dr. Robert X. Gao is an Associate Professor of Mechanical Engineering at the University of Massachusetts Amherst, USA. He received his B.S. degree from China, and his M.S. and Ph.D. from the Technical University Berlin, Germany, in 1982, 1985, and 1991, respectively. Since starting his academic career in 1992, he has been conducting research in the general area of embedded sensors and sensor networks, "smart" electromechanical systems, wireless data communication, and signal processing for machine health monitoring, diagnosis, and prognosis. Dr. Gao has published over 100 refereed papers on journals and international conferences, and has one US patent and two pending patent applications on sensing. He is an Associate Editor for the IEEE Transactions on Instrumentation and Measurement, and served as the Guest Editor for the Special Issue on Sensors of the ASME Journal of Dynamic Systems, Measurement, and Control, published in June, 2004. Condition-based Monitoring and Control for Intelligent Manufacturing has arisen from the Flexible Automation and Intelligent Manufacturing (FAIM 2004) conference, held in Toronto, Canada on July12-14 2004. Thirty papers have been selected out of 170 presented at the conference and the authors of these papers have been invited to submit extended updated versions of these papers in order to create a state of the art review of condition-based monitoring and control in manufacturing.