We present a trainable system for locating clothed people in photographic images. People detection is a particularly challenging image understanding problem; as a result of variations in clothing and posture, the appearance of people may vary enormously from image to image. Our approach attempts to construct a maximally person-like assembly of image regions, where candidate regions are provided by color-based segmentation followed by non-purposive grouping. A tree structured probability model is employed to allow efficient searches. This structure represents the pairwise configuration of body parts as a function of relative position, relative size, and adjacency. Face and skin detection is also used to help the search. The problem of occlusion is addressed through a mixture of trees, where the different mixture components represent the possible subsets of visible parts. Different clothing styles are accounted for by separate models. Experimental results are shown to demonstrate the promise of and challenges for the current system.