This paper presents an algorithm for the solution of nonconvex mixed integer nonlinear programming (MINLP) problems involving general constraints and objective functions. The algorithm employs a symbolic reformulation step that brings the original MINLP problem to an equivalent standard form for which a convex relaxation can be constructed. The reformulated problem is then solved using a spatial branch-and-bound algorithm which branches on both integer and continuous variables. Issues relating to the efficient implementation of this algorithm and its parallelisation are also discussed. The algorithm has been incorporated within the gPROMS process modelling environment and tested on several MINLP problems arising from process engineering applications.