Given a set of dissimilarities data between n objects, multidimensional scaling is the problem of reconstructing a geometrical pattern of these objects, using n points, so that between-points distance corresponds to between-objects dissimilarity. Often, the collection of input data requires rating the dissimilarities between all n(n - 1)/2 possible pairs of stimuli. When the number of stimuli is large, say n $ 30, the number of pairs to be compared becomes very large and the similarity task inefficient. Hence a question of major importance is how to increase the efficiency of the similarity task while maintaining satisfactory scaling solutions. This article reviews the main similarity task methods suitable for a large objects set.