Image retrieval remains a difficult task, in spite of the many research efforts applied over the past decade or more, from IBM's QBIC onwards. Colour, texture and shape have all been used for content-based image retrieval (CBIR); texture is particularly effective, alone or with colour. Many researchers have expressed the hope that textures can be organised and classified in the way that colour can; however, it seems likely that such an ambition is unrealisable. While the goal of content-based retrieval is to retrieve "more images like this one," there is the difficulty of judging what is meant by similarity for images. It seems appropriate to search on what the images actually look like to potential users of such systems. No single computational method for textural classification matches human perceptual similarity judgements. However, since different methods are effective for different kinds of textures, a way of identifying or grouping such classes should lead to more effective retrievals. In this research, working with the Brodatz texture images, participants were asked to select up to four other textures which they considered similar to each of the Brodatz textures in turn. A principal components analysis was performed upon the correlations between their rankings, which was then used to derive a 'mental map' of the composite similarity ranking for each texture. These similarity measures can be considered as a matrix of distances in similarity space; hierarchical cluster analysis produces a perceptually appropriate dendrogram with eight distinct clusters.