Since　support　vector　machines　(SVM)　exhibit　a　good　generalization　performance　in　the　small　sample　cases,　these　have　a　wide　application　in　machinery　fault　diagnosis.　However,　a　problem　arises　from　setting　optimal　parameters　for　SVM　so　as　to　obtain　optimal　diagnosis　result.　This　article　presents　a　fault　diagnosis　method　based　on　SVM　with　parameter　optimization　by　ant　colony　algorithm　to　attain　a　desirable　fault　diagnosis　result,　which　is　performed　on　the　locomotive　roller　bearings　to　validate　its　feasibility　and　efficiency.　The　experiment　finds　that　the　proposed　algorithm　of　ant　colony　optimization　with　SVM　(ACO-SVM)　can　help　one　to　obtain　a　good　fault　diagnosis　result,　which　confirms　the　advantage　of　the　proposed　ACO-SVM　approach.