Multiple　sound　source　localization　is　a　hot　topic　of　concern　in　recent　years.　In　this　paper,　a　multi-source　localization　method　based　on　weight　clustering　and　outlier　removal　is　proposed　to　deal　with　the　multiple　source　localization　in　the　environment　with　high　reverberation　time.　In　this　kind　of　environments,　there　are　always　some　T-F　points　consisting　of　components　from　multiple　sources　mixed　in　the　detected　spares　components.　These　T-F　points,　which　are　called　outliers,　usually　carry　the　wrong　information　of　localization　and　could　lead　to　the　decline　of　localization　accuracy.　To　solve　this　problem,　the　Point　Offset　Residual　Weight　(PORW)　and　Source　Offset　Residual　Weight　(SORW)　are　introduced　to　measure　the　contribution　of　each　T-F　point　to　the　localization.　The　binary　clustering　is　proposed　to　distinguish　and　remove　the　outliers.　After　that,　a　statistical　histogram　of　DOA　estimation　is　drawn　using　the　composite　weight　to　weaken　the　effect　of　components　that　interfere　with　the　localization.　Finally,　the　multi-source　localization　is　conducted　through　peak　searching.　The　objective　evaluation　of　the　proposed　method　is　conducted　in　various　simulated　environments.　The　results　show　that　the　proposed　method　achieves　a　better　performance　compared　with　the　reference　methods　in　sources　localization.　©　2021　APSIPA.