In this paper. Laguerre filters and simple polynomials are used respectively as linear and nonlinear parts of a Wiener structure. The obtained model structure is the so-called Wiener-Laguerre model. This model is used to evaluate identification of a pH neutralization process. Then the model is used in a nonlinear model predictive control framework based on the sequential quadratic programming (SQP) algorithm. Various orders of Laguerre filters and nonlinear polynomials are tested, and the results are compared for the validation of these models. Validation results for various orders suggest that in order to have a good trade-off between simplicity of the model and its corresponding fitness, a second order nonlinear polynomial along with two Laguerre filters may be selected. The fitness of this model according to variance account for (VAF) criterion is 92.32%. which is completely acceptable for nonlinear model predictive control applications. Then the identified Wiener-Laguerre model is used for nonlinear model predictive control and the results are compared with model predictive control in which just Wiener model was used for identification. It is shown that the use of the Wiener-Laguerre structure improves the quality of modeling together with the rate of convergence of SQP in a reasonable time. Furthermore, these results are also compared with the performance of a linear model predictive controller based on Laguerre model to provide a fair comparison between linear and nonlinear systems. (C) 2008 Elsevier B.V. All rights reserved.