An efficient approach to recognize distance-invariant appearing in outdoor and indoor scenes is introduced. The differences of the sizes of object images caused by varying distances are normalized by a model-based subsampling of images. The distance-invariant images both simplify and due to their reduced number of pixels help to accelerate object recognition. This model-based subsampling has been used for creating a database of distance-independent representations of various objects allowing the subsequent recognition of such objects in real time. An interactive user interface with a learning ability was provided to facilitate the introduction of new objects into the database. A number of algorithms for recognizing objects were implemented and evaluated. They employ different forms of object representations and were analyzed regarding their effectiveness for recognizing objects in varying distances. In experiments two of the investigated recognition methods, one based on cross correlation and the other one on user-defined edges, appeared suitable for realizing a fairly reliable object recognition in real time, as required by autonomous vehicles and mobile robots.