Relevance feedback visualisation (RFV) is a technique developed to visualise the feature values of returned results in a content-based image retrieval system that incorporates relevance feedback. RFV is used also to re-sort retrieved results according to user requirements, enable the interactive investigation of pertinent features and permit the discovery of otherwise unidentifiable trends in the dataset. When large numbers of features are involved, manually determining which feature attribute graphs are the most important can be a burdensome task. In this paper, a method for automatically sorting attribute graphs according to their significance in the search operation is introduced. The result is that features worthy of further investigation are immediately identified, the user interface is improved, and the CBIR system is made more effective.