Data from a paperboard machine were used to compare the performance of linear partial least squares (PLS) and nonlinear feed-forward neural network (FFNN) modeling of a continuous process. Fifteen selected variables were used as input parameters to the models. while the quality class of the manufactured product was the output response. The models were validated with external data different to those used in the design of the models. Evaluation with root mean square error of prediction (RMSEP) showed that the FFNN models were better for prediction than the PLS models. For monitoring, however. the PLS models detected deviations from normal settings in the paperboard machine more sensitively than the FFNN models. It is suggested that these findings have general relevance to other continuous processes in manufacturing industries too.