Complementarity among classifiers is a crucial aspect in classifier combination. A combined classifier is significantly superior to the individual classifiers only if they strongly complement each other. In this paper a complementarity-based analysis of sets of classifier is proposed for investigating the behaviour of multi-classifier systems, as new classifiers are added to the set. The experimental results confirm the theoretical evidence and allow the prediction of the performance of a multi-classifier system, as the number of classifiers increases.