Wafer　map　defect　pattern　(WMDP)　recognition　is　critical　to　evaluate　the　semiconductor　manufacturing　process.　Traditional　WMDP　recognition　algorithms,　such　as　deep　learning-based　methods,　only　focus　on　improving　the　accuracies　of　the　seen　patterns　in　the　training　set　but　ignore　the　recognition　and　incremental　generalization　of　new　emerging　patterns.　The　model　can　only　be　retrained　by　using　the　overall　dataset　composed　of　new　and　old　patterns　to　gain　the　ability　to　recognize　the　new　patterns.　To　address　the　above　problems,　this　paper　proposes　a　WMDP　recognition　method　based　on　self-organizing　incremental　neural　network.　The　proposed　method　detects　abnormal　patterns　according　to　the　relationship　between　input　response　and　adaptive　threshold　of　winner　neurons,　updates　the　model　only　with　new　pattern　samples　by　dynamically　adjusting　the　number　or　connection　of　neurons.　Experiments　show　that　the　proposed　method　has　the　ability　to　classify　known　patterns　and　discover　unknown　abnormal　patterns.　Its　unique　competitive　learning　mechanism　makes　it　possible　to　update　the　model　only　with　new　pattern　samples　without　catastrophic　forgetting.　©　2021　IEEE