The quality of an object localization depends essentially on the adequate selection of a suitable reference. In most computational approaches developed so far only the distance between the located object and a potential reference object has been used as a decision criterion. However many other criteria have to be considered for a cognitively plausible selection of adequate reference points. In this paper we investigate how object and context dependent properties, like referentiality, visual salience, functional dependencies, or prior knowledge, influence the quality of a reference object. Each factor is quantitatively determined and scaled by relevance to a certain context. The scaling permits the necessary comparability of the different quality criteria. Finally, on the basis of these factors a computational model is presented which permits a context dependent determination of the best reference object in a particular spatial configuration.