The partial least squares (PLS) methodology is a regression tool able to deal with two important problems in system identification: the noise contained in the industrial data and the correlations that are observed among the input variables. In this article, the PLS technique is applied on real industrial data to produce dynamical multi-input single-output (MISO) models of the behavior of the kappa number in a continuous digester with respect to the important input variables that can be measured on-line. The ultimate goal is the development of a model predictive control (MPC) scheme that can be used for keeping the kappa number of the produced pulp close to the desired set point. We show that a modi. cation in the standard MPC algorithm is needed in order to take into account the correlations among the input variables.