The　effluent　total　phosphorus　(ETP)　is　an　important　parameter　to　evaluate　the　performance　of　wastewater　treatment　process　(WWTP).　In　this　study,　a　novelmethod,　using　a　data-derived　soft-sensormethod,　is　proposed　to　obtain　the　reliable　values　of　ETP　online.　First,　a　partial　least　square　(PLS)　method　is　introduced　to　select　the　related　secondary　variables　of　ETP　based　on　the　experimental　data.　Second,　a　radial　basis　function　neural　network　(RBFNN)　is　developed　to　identify　the　relationship　between　the　related　secondary　variables　and　ETP.　This　RBFNN　easily　optimizes　the　model　parameters　to　improve　the　generalization　ability　of　the　soft-sensor.　Finally,　a　monitoring　system,　based　on　the　above　PLS　and　RBFNN,　named　PLS-RBFNN-based　soft-sensor　system,　is　developed　and　tested　in　a　real　WWTP.　Experimental　results　show　that　the　proposed　monitoring　system　can　obtain　the　values　of　ETP　online　and　own　better　predicting　performance　than　some　existing　methods.　(c)　2017　The　Chemical　Industry　and　Engineering　Society　of　China,　and　Chemical　Industry　Press.　All　rights　reserved.