Which processes underly our ability to quickly recognize familiar objects within a complex visual input scene? In this paper an imple(cid:173) mented neural network model is described that attempts to specify how selective visual attention, perceptual organisation, and invari(cid:173) ance transformations might work together in order to segment, select, and recognize objects out of complex input scenes containing multi(cid:173) ple, possibly overlapping objects. Retinotopically organized feature maps serve as input for two main processing routes: pathway' dealing with location information and the'what-pathway' computing the shape and attributes of objects. A location-based at(cid:173) tention mechanism operates on an early stage of visual processing selecting a contigous region of the visual field for preferential proces(cid:173) sing. Additionally, location-based attention plays an important role for invariant object recognition controling appropriate normalization processes within the what-pathway.