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Standard statistical discriminant analysis techniques inherently make assumptions about underlying class structures in data, limiting their validity and effectiveness. Other classification methods, such as soft independent modeling of class analogy (SIMCA) or artificial neural networks, replace the disadvantage of making such assumptions with an equally impeding lack of interpretability. The intention of this work was to formulate a classification scheme that avoids these and other obstacles. A new classification technique has been designed that combines the recursive partitioning feature of tree-based classifiers (e.g. classification and regression trees (CART)) with the probabilistic reasoning of Bayesian networks. The proposed hybrid approach benefits from all the advantages of tree-based classifiers and Bayesian networks without experiencing the usual limitations associated with these methods individually. The resulting classifier outperformed several standard methods and has the added benefits of being both statistically and semantically justifiable. Copyright © 1999 John Wiley & Sons, Ltd.