Motion segmentation involves clustering features together that belong to independently moving objects. The image features on each of these objects conform to one of several putative motion models, but the number and type of motion is unknown a priori. In order to cluster these features, the problems of model selection, robust estimation and clustering must all be addressed simultaneously. Within this paper we place the three problems into a common statistical framework; investigating the use of information criteria and robust mixture models as a principled way for motion segmentation of images. The final result is a general fully automatic algorithm for clustering that works in the presence of noise and outliers.