In this paper, a data-based control method for reducing product quality variations in batch pulp digesters is presented. Compared to the existing techniques, the new technique uses more liquor measurements in predicting the final pulp quality. The liquor measurements obtained at different time instances during a cook are related to the final pulp quality through a partial least squares (PLS) regression model. In using the PLS regression model for control, two approaches an proposed. In the first approach, optimal control moves are computed directly using the PLS model, while the second approach employs a nonlinear H-factor model of which parameters are adapted using the prediction from the PLS model. The effectiveness of the prediction and control algorithms is examined through simulation studies. Experimental study is then performed on a lab-scale batch digester, to test the effectiveness of the prediction performance of the PLS model. The control algorithms will be tested on the experimental set-up in the future. (C) 2000 IFAC.