Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models tailored to solving multi-dimensional classification problems, where an instance has to be assigned to multiple class variables. In this paper, we propose a novel multi-dimensional classifier that consists of a classification tree with MBCs in the leaves. We present a wrapper approach for learning this classifier from data. An experimental study carried out on randomly generated synthetic data sets shows encouraging results in terms of predictive accuracy.