While a variety of multiple classifier systems have been studied since at least the late 1950’s, this area came alive in the 90’s with significant theoretical advances as well as numerous successful practical applications. This article argues that our current understanding of ensemble-type multiclassifier systems is now quite mature and exhorts the reader to consider a broader set of models and situations for further progress. Some of these scenarios have already been considered in classical pattern recognition literature, but revisiting them often leads to new insights and progress. As an example, we consider how to integrate multiple clusterings, a problem central to several emerging distributed data mining applications. We also revisit output space decomposition to show how this can lead to extraction of valuable domain knowledge in addition to improved classification accuracy.