An inductive data mining algorithm based on genetic programming, GPForest, is introduced for automatic construction of decision trees and applied to the analysis of process historical data. GPForest not only outperforms traditional decision tree generation methods that are based on a greedy search strategy therefore necessarily miss regions of the search space, but more importantly generates multiple trees in each experimental run. In addition, by varying the initial values of parameters, more decision trees can be generated in new experiments. From the multiple decision trees generated, those with high fitness values are selected to form a decision forest. For predictive purpose, the decision forest instead of a single tree is used and a voting strategy is employed which allows the combination of the predictions of all decision trees in the forest in order to generate the final prediction. It was demonstrated that in comparison with decision tree methods in the literature, GPForest gives much improved performance. (C) 2009 Elsevier Ltd. All rights reserved.