We analyze data from a gamut-mapping experiment using several statistical procedures for ranked data. In this experiment six gamut-mapping algorithms were applied to six different images and the results were ranked by 31 judges according to how well the images matched an original. We fitted two distance-based statistical models to the data: both analyses showed that aggregate preference among the six algorithms depended on the image viewed. Based on the first model we classified the images into four classes or clusters. We applied unidimensional unfolding, a technique from mathematical psychology, to extract latent reference frames upon which judges plausibly ordered the algorithms. Four color experts gave interpretations of the derived reference frames. We used the second model to generate confidence sets for the consensus rankings, and another cluster analysis.