Multivariate statistical process control (MSPC) based on principal component analysis (PCA) and partial least squares (PLS) regression was simulated, based on industrial data collected over a two-year period within a plant producing wood pellets as biofuel. The data used in the simulations consisted of values of five variables of analysed intermediate products (sawdust and powder) and end products (pellets), acquired during processes with seven on-line settings of controls. PCA global modelling revealed an overlap in the data between years and detected three different pellet types. Correlations within the dataset indicated there was a time lag of up to 14 h. Therefore, PLS prediction of current product values was based on observations containing the current process settings and all variable values within a preceding 18 h time interval. Global models showed that predictions of the dryness of sawdust, milled sawdust and pellets had good accuracy, whereas predictions of pellet bulk density and mechanical durability were less accurate. Dynamic and local PLS modelling showed that more accurate predictions of pellet dryness were obtained if all previous observations were included in the calibration set rather than observations in calibration windows of the 10 or 100 preceding observations. The results illustrate the possibilities to implement MSPC in the wood pellet industry, potentially handling huge amounts of data. To develop and implement the next phase of process control more parameters must be included in the MSPC models, e.g. data acquired using on-line instruments to continuously collect information on variations in the stream of material. (c) 2012 Elsevier Ltd. All rights reserved.