A robotic vehicle needs to understand the terrain and features around it if it is to be able to navigate complex environments such as road systems. By taking advantage of the fact that such vehicles also need accurate knowledge of their own location and orientation, we have developed a sensing and object recognition system based on information about the area where the vehicle is expected to operate. The information is collected through aerial surveys, from maps, and by previous traverses of the terrain by the vehicle. It takes the form of terrain elevation information, feature information (roads, road signs, trees, ponds, fences, etc.) and constraint information (e.g., one-way streets). We have implemented such an a priori database using One Semi-Automated Forces (OneSAF), a military simulation environment. Using the Inertial Navigation System and Global Positioning System (GPS) on the NIST High Mobility Multi-purpose Wheeled Vehicle (HMMWV) to provide indexing into the database, we extract all the elevation and feature information for a region surrounding the vehicle as it moves about the NIST campus. This information has also been mapped into the sensor coordinate systems. For example, processing the information from an imaging Laser Detection And Ranging (LADAR) that scans a region in front of the vehicle has been greatly simplified by generating a prediction image by scanning the corresponding region in the a priori model. This allows the system to focus the search for a particular feature in a small region around where the a priori information predicts it will appear. It also permits immediate identification of features that match the expectations. Results indicate that this processing can be performed in real time.