An object verification and localization system should answer the question whether an expected object is present in an image or not, i.e. verification, and if present where it is located. Such a system would be very useful for mobile robots, e.g. for landmark recognition or for the fulfilment of certain tasks. In this paper, we present an object verification and localization system specially adapted to the needs of mobile robots. The object model is based on a collection of local features derived from a small neighbourhood around automatically detected interest points. The learned representation of the object is then matched with the image under consideration. The tests, based on 81 images, showed a very satisfying tolerance to scale changes of up to 50%, to viewpoint variations of 20, to occlusion of up to 80% and to major background changes as well as to local and global illumination changes. The tests also showed that the verification capabilities are very good and that similar objects did not trigger any false verification.