ecognizing the reappearance of an object provides a useful functionality for automated scene interpretation.While many modalities will in general contribute to this task, this paper focuses on the use of object color properties. Re-identifying objects between cameras requires a calibration of the respective color spaces, which are a function of both camera and lighting properties. Ways in which such calibration may be accomplished, on-line and without specialist equipment, are investigated. Matching against previously observed objects is an image retrieval problem, for which we develop an explicit representation of the observed color distribution. Color histograms can be built and compared in real time without specialist hardware: we investigate effective representations for classification of object identity. The camera capture noise characteristics are used to define optimal histogram quantization intervals. A model of the object color properties is built using multiple observations of the same object, acquired with the use of a spatial object tracker. Comparative results on real data are presented for single and multi-camera re-identification, using algorithms which may be executed in real time.