The case-based reasoning (CBR) and case-based design (CBD) have been around for some time and established themselves as one of the commonly used mechanisms of approximate reasoning in intelligent systems and de- sion support systems, in particular. In a nutshell, the CBR mechanisms offer a powerful and general environment in which we generalize on a basis of - ready accumulated experience being represented in the form of afinite and relatively small collection of cases. Those cases constitute the essence of the existing domain knowledge. When encountering a new situation we invoke and eventually modify the already collected decision scenarios (cases) and arrive at the pertinent decision or a certain design alternative. Interestingly, uncertainty or granularity of resulting decision is inherently associated with the nature of the cases being used in the reasoning process and a way in which partial matching takes place between the historical findings (cases) and a current evidence. The book by Professors Avramenko and Kraslawski is unique in several important ways. First, it is an impressive and in-depth treatment of the essence of the case-based reasoning strategy and case-based design dwelling upon the algorithmic facet of the paradigm. Second, the authors provided an excellent applied research framework by showing how this development can be effectively utilized in realword complicated environment of process engineering–a pursuit that is rarely reported in the literature in such a comprehensive m- ner as done in this book.