In　case-based　reasoning　(CBR)　classification　systems,　the　similarity　metrics　play　a　key　role　and　directly　affect　the　system＇s　performance.　Based　on　our　previous　work　on　the　learning　pseudo　metrics　(LPM),　we　propose　a　case-based　reasoning　method　for　pattern　classification,　where　the　widely　used　Euclidean　distance　is　replaced　by　the　LPM　to　measure　the　closeness　between　the　target　case　and　each　source　case.　The　same　type　of　case　as　the　target　case　can　be　retrieved　and　the　category　of　the　target　case　can　be　defined　by　using　the　majority　of　reuse　principle.　Experimental　results　over　some　benchmark　datasets　and　a　fault　diagnosis　of　the　Tennessee-Eastman　(TE)　process　demonstrate　that　the　proposed　reasoning　techniques　in　this　paper　can　effectively　improve　the　classification　accuracy,　and　the　LPM-based　retrieval　method　can　substantially　improve　the　quality　and　learning　ability　of　CBR　classifiers.　(C)　2017　Elsevier　Ltd.　All　rights　reserved.