Because of the lack of a clear guideline or technique for selecting classifiers which maximise diversity and accuracy, the development of techniques for analysing classifier relationships and methods for generating good constituent classifiers remains an important research direction. In this paper we propose a framework based on the Bayesian Belief Networks (BBN) approach to classification. In the proposed approach the multiple-classifier system is conceived at a meta-level and the relationships between individual classifiers are abstracted using Bayesian structural learning methods. We show that relationships revealed by the BBN structures are supported by standard correlation and diversity measures. We use the dependency properties obtained by the learned Bayesian structure to illustrate that BBNs can be used to explore classifier relationships, and for classifier selection.