In this paper we describe a method for structure-from-motion recovery when there are multiple objects in the scene. We use our recently developed recursive estimation technique to recover shape and motion parameters given a set of features being tracked in the image. Support maps defined for these features limit the integration of information across space when there are multiple objects in a scene, due to occlusions or interleaved regions. Multiple hypothetical models are run concurrently, based on random initial groupings of the data. A minimum description length selection mechanism determines which 3-D structure/motion models and which groups of features constitute the best match to the data. We show results segmenting a synthetic sequence containing features on two rotating spheres, where there are no static cues available for segmentation, and on a real image sequence containing both camera and object motion.