Neural networks have been applied within manufacturing domains, in particular electronics industries, to address the inherent complexity, the large number of interacting process features and the lack of robust analytical models of real industrial processes. The ability of neural systems to provide nonlinear mappings between process features and desired outputs has been the major driving force behind implementations. One of the major issues limiting the widespread industrial uptake of neural systems is the lack of detailed understanding of their design, implementation and operation. In many cases, network topologies and training parameters are systematically varied until satisfactory convergence is achieved. There is little discussion of the rationale behind the adopted training methods. A review of research into the functions that can be readily represented by neural networks are presented in this paper. The application focus is the control and monitoring of a discrete manufacturing process that is part of the manufacturing cycle of mixed technology surface mount printed circuit boards. Detailed knowledge of the process operation and functionality that can be represented by simple network topologies have been combined to develop a structured, partially interconnected neural network that provides optimized convergence performance. A comparison of the designed solution with standard approaches to neural network implementation is given. It has been demonstrated that if there is sufficient confidence in the operation of the process, input feature interaction within the network can be constrained to produce a robust control and monitoring system.