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This paper presents an approach for aspect-based retrieval of 3D models or circular views of real objects. The objects in our database are described by a set of images, which are taken varying the pose of a constant angle along a given plane. A set of parameters is computed for each image, taking into account general features like bounding boxes, contours, and mass distribution. The number of parameters of each image is then reduced using principal component analysis, obtaining a low dimensional feature space. We assume that the query is in the same form of objects in our database, and thus it can undergo the same feature extraction process. The system then retrieves the most similar objects computing the L1 distance in the features space. Preliminary results are presented with a database of 100 different objects, consisting of 2000 different views.