Case　retrieval　is　a　major　step　in　case-based　reasoning　(CBR),　which　seeks　the　most　similar　historical　case　to　correspond　to　the　target　case.　However,　the　first　step　of　similarity　measurement　is　to　determine　the　weights　of　attributes,　which　would　affect　the　accuracy　of　the　similarity　calculation　results.　In　this　study,　we　propose　a　new　method,　called　DEA-CBR　that　integrates　the　double　frontiers　data　envelopment　analysis　(DEA)　to　determine　the　most　similar　historical　case　based　on　the　similarity　efficiency　of　each　historical　case.　This　proposed　method　is　different　from　the　traditional　distance-based　similarity　measurement　methods　in　that　attribute　weights　are　determined　by　DEA　models　without　the　need　to　be　specified.　The　proposed　DEA-CBR　approach　first　defines　attribute　distances　between　each　historical　case　and　target　case　to　calculate　attribute　similarity　for　each　attribute.　The　maximum　and　the　minimum　similarity　efficiencies　of　each　historical　case　are　then　measured　with　DEA　models　and　are　geometrically　averaged　to　measure　the　overall　similarity　efficiency　of　each　historical　case,　based　on　which　the　most　similar　historical　case　can　be　determined.　Two　numerical　examples　are　provided　to　illustrate　the　potential　applications　and　benefits　of　the　proposed　DEA-CBR　method.　©　2019　-　IOS　Press　and　the　authors.　All　rights　reserved.