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Objective Due to the incompleteness and complexity of fault diagnosis for power transformers, a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented. Fuzzy set theory is used both for representation of incipient faults indications and producing a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space. The fuzzy membership functions corresponding to the indicative regions, modelled by rules, are stored as cases. Results Diagnostic conclusions are made using a similarity measure based on these membership functions. Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis. Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.