Biochemical　oxygen　demand　(BOD)　is　an　important　index　for　evaluating　water　quality,　and　a　variable　directly　controlled　in　the　wastewater　treatment　process.　To　improve　the　performance　of　wastewater　treatment,　it　is　necessary　to　find　out　an　effective　method　for　measuring　BOD.　This　paper　presents　a　new　soft　measurement　which　can　provide　guaranteed　estimation　of　the　effluent　BOD.　The　principal　component　analysis　is　utilized　to　select　the　secondary　variables　for　the　soft　sensor.　In　virtue　of　its　simple　topological　structure　and　universal　approximation　ability,　the　radial　basic　function　neural　network　(RBFNN)　is　utilized　in　the　soft　sensor　modeling.　Considering　the　bounded　modeling　error,　linear-in-parameters　set　membership　identification　algorithm　is　used　to　obtain　a　description　of　the　uncertain　set　of　the　output　weights　after　the　determination　of　centers　of　the　RBFNN.　The　RBFNN　model　with　uncertain　output　weights　can　predict　the　upper　and　lower　bounds　of　the　effluent　BOD　during　the　wastewater　treatment.　Besides,　a　bundle　of　soft　sensors　is　constructed　and　the　intersection　of　the　results　given　by　the　soft　sensors　is　used　to　lower　the　conservatism　by　using　a　single　sensor.　Experiment　results　show　the　satisfying　performance　of　the　proposed　method.　©　2018,　Editorial　Board　of　Journal　of　Harbin　Institute　of　Technology.　All　right　reserved.