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We present a so-called neural map, a novel memory framework for visual object recognition and categorization systems. The properties of its computational theory include self-organization and intelligent matching of the image features that are used to build their object models. Its performance for representing the visual object knowledge comprised by these models and for recognizing unknown objects is measured using three different types of image features, which extract different granularity of information from object views of the ETH-80 image set. The obtained experimental results slightly outperform previous ones using PCA-based methods on the same image set, and they suggest that the medium-sized image features maximize the object models' informativeness and distinctiveness.