The detection of objects in every frame of a sequence is often not sufficient for scene interpretation. Tracking can increase the robustness, especially when occlusions occur or when objects temporally disappear. The standard approach for tracking is to use a Kalman filter for every object. This, however, requires the use of a high complexity management system to deal with the multiple hypotheses necessary to track all anticipated objects. In this paper we present a stochastic approach which is based on the Condensation algorithm -- conditional density propagation over time -- that is capable of tracking multiple objects with multiple hypotheses in range images. A probability density function describing the likely state of the objects is propagated over time using a dynamic model. The measurements influence the probability function and allow the incorporation of new objects into the tracking scheme. Additionally, the representation of the density function with a fixed number of samples ensures a constant running time per iteration step. Results on different data sources are shown for mobile robot applications.