The recent advances in mathematical programming approaches applied to process design and operation problems have produced a need for the ability to find the global optimum of a nonconvex problem containing discrete : variables (a nonconvex MINLP). This paper presents a modified version of the reformulation/spatial branch-and-bound algorithm of Smith and Pantelides (1996) for the solution of such problems. The algorithm is implemented within the gPROMS modelling environment (Barton and Pantelides, 1994) and tested on several MINLP problems arising from process engineering applications.