In　order　to　automatically　classify　weld　surface　defect　in　header　pipe　joint,　Computer　Vision　based　defect　classification　is　studied.　The　texture　features　of　different　weld　defects　are　analyzed,　Grey　level　co-occurrence　matrix　(GLCM)　is　applied　to　extract　features　from　digital　images,　and　15　types　of　statistical　indexes　are　obtained　to　characterize　the　weld　surface　defects.　Back-propagation　artificial　neural　network　method　is　used　for　defect　classification.　The　influence　of　GLCM　parameters,　the　neural　network　structure　and　the　number　and　variety　of　input　parameters　on　the　defect　classification　performance　is　analyzed,　and　optimal　neural　network　structure　and　input　parameters　are　selected.　In　further,　the　optimized　network　is　utilized　for　training　and　classifying　the　images　of　different　weld　defects　acquired　by　industrial　endoscope.　The　results　show　that　weld　defects　detection　rate　of　overall　classification　can　be　up　to　91%.　The　proposed　method　can　be　used　for　automatic　classification　of　weld　surface　defect　in　header　pipe　joint.　©　2017,　Science　Press.　All　right　reserved.