The non-linear behaviour exhibited by many industrial processes can be usefully modelled with the techniques of computational intelligence: neural networks; fuzzy systems and nonlinear partial least squares. Soft Sensors for Monitoring and Control of Industrial Processes underlines the real usefulness of each approach and the sensitivity of the individual steps in soft-sensor design to the choice of one or the other. Design paths are suggested and readers shown how to evaluate the effects of their choices. The case studies reported, resulting from collaborations between the authors and a number of industrial partners, raised challenging measuring problems. The applications of soft sensors presented in this book cope with the whole range from measuring system backup through real-time prediction for plant control to sensor diagnosis and validation. Some of the soft sensors developed here are implemented on-line at industrial plants. Data sets for some of the case studies can be downloaded from springer.com.