We show that large ensembles of (neural network) models, obtained e.g. in bootstrapping or sampling from (Bayesian) probability distributions, can be effectively summarized by a relatively small number of representative models. We present a method to find representative models through clustering based on the models’ outputs on a data set. We apply the method on models obtained through bootstrapping (Boston housing) and on a multitask learning example.