This research presents guidelines to design control processes where improving quality is achieved by improving the manufacturing consistency through the use of intelligent process control. Conventional control processes cannot include the theoretical knowledge, experimental knowledge, and expert knowledge available concerning the product. A hybrid intelligent process control (IPC) combining a continuous simulation (CS) and an artificial neural network (ANN) can make this knowledge available to the operator for process control. This paper presents a methodology for combining the CS and ANN to achieve real-time process control. A human-machine interface (HMI) is included in the process to aid operators in communication with the CS/ANN hybrid IPC. The result of the new process is a real-time process control advisor (RTPCa). A case example for the methodology of formulating, formalizing, validating, and evaluating the RTPCa is given. The case studied concerns galvanizing continuous sheet steel at a steel plant. The CS is written in SIM AN, and the ANN in C. The research validates and evaluates the RTPCa using plant data, simulation output, and face validation by plant personnel. The authors conclude that the benefits of the RTPCa over other forms of IPC include better process communication to the operator, robustness to moderate changes in system parameters, the flexibility to retrain the ANN if conditions change dramatically, and the computation speed necessary for real-time process control. This methodology has further applications to other continuous processes where quality is determined by manufacturing consistency of the product, such as in the pulp paper and film processing industries.