in this work a prioritized multiobjective model predictive control configuration for nonlinear processes is proposed. The process is modeled by an adaptive radial basis function neural network so that modifications through time can be identified. The different control targets are formulated in a multiobjective optimization problem which is solved using a prioritized evolutionary algorithm. The request for adequate information in order to adapt the dynamics of the model is considered as the top priority objective. The algorithm is tested through the control of a pH reactor and the results are in favor of the proposed methodology.