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In a previous work, we presented an approach for shape-based image retrieval using the curvature scale space (CSS) and self-organizing map (SOM) methods. Here, we examine the robustness of the representation with images under different scales. The shape features of images are represented by CSS images extracted from, for example, a large database and represented by median vectors that constitutes the training data set for a SOM neural network which, in turn, will be used for performing efficient image retrieval. Experimental results using a benchmark database are presented to demonstrate the usefulness of the proposed methodology. The evaluation of performance is based on accuracy and retrieval time assessed in the framework of a Monte Carlo experience.