The focus of this paper is on computer-aided process planning (CAPP) for parts manufacture in systems of definite processing capabilities, involving multi-axis machining centers. A methodical approach is developed to optimally solve for process planning problems, which consists in the identification of process alternatives and sequencing working steps. The approach involves the use of the branch and bound (B&B) concept from the field of artificial intelligence (AI). A conceptual scheme for generation of alternative process plans in the form of a network is developed, based on part design data modeling in terms of machining features. A relevant algorithm is proposed for creating such a network and searching for the optimal process plan solution from the viewpoint of its operational performance, under formulated process constraints. The use of the approach is studied numerically with regard to a real life case study and diverse machine tools with relevant tooling are considered. Generated process alternatives for complex machining with given systems are studied using models programmed in the Matlab environment.