The automated interpretation of aerial image data is a task with increasing significance for several applications, e.g. quality control and automatic updating of GIS data, automatic land use change detection, measurement of sealed areas for public authority uses, monitoring of land erosion etc. The use of additional sensors could improve the performance of the automated classification; however, because of additional costs or simple unavailability of data, this approach should be avoided. One possibility to stabilize an automatic image analysis is using remote sensing data of the same region of different dates that is often existing. This paper presents a method how a monotemporal knowledge representation can be expanded by a temporal component to take advantage of previous classifications of the same scene and knowledge about the time dependency of the object classes. The present approach proposes the combination of a semantic network, representing the generic description of the scene, and a state transition diagram, modeling the possible state transitions for each one of the classes of interest. The probabilities of the state transition diagram are introduced as a priori knowledge in a statistical classification procedure. Experimental results from a series of three aerial images from 1983 up to 2001 of a suburban region near Hannover are shown in order to illustrate the potential of the proposed multitemporal approach. © 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved.