Multiple classifiers consist of sets of subclassifiers, whose individual predictions are combined to classify new objects. These approaches attract an interest of researchers as they can outperform single classifiers on wide range of classification problems. This paper presents an experimental study of using the rule induction algorithm MODLEM in the multiple classifier scheme called combiner, which is a specific meta learning approach to aggregate answers of component classifiers. Our experimental results show that the improvement of predictive accuracy depends on the independence of errors made by the base classifiers. Moreover, we summarise our experience with using MODLEM as component in other multiple classifiers, namely bagging and n2 classifiers.