The use of one or more monocular images to estimate the three-dimensional position of objects is investigated. The identities of the objects are known, and geometric models are assumed to be available. Linear features extracted from sensor data are interpreted as corresponding with model features by search of an interpretation tree built using prior position estimates. Object positions are updated by maximum-likelihood estimation. Position estimation results from an implemented system are presented, demonstrating the location of partially occluded objects in a cluttered scene.>