Wiener models, consisting of a linear dynamic element followed in series by a static nonlinear element, are considered to be ideal for representing a wide range of nonlinear process behavior. They are relatively simple models requiring little more effort in development than a standard linear model, yet offer superior characterization of systems with highly nonlinear gains. Wiener models may be incorporated into model predictive control (MPC) schemes in a unique way which effectively removes the nonlinearity from the control problem, preserving many of the favorable properties of linear MPC. This paper examines various model structures including ARX and step-response models with polynomial or spline nonlinearities and their corresponding identification strategies. These techniques are then applied to an experimental pH neutralization process where the performance of Wiener MPC is compared with that of the linear MPC and the benchmark PID control to showcase the salient features of this new approach.