In　this　paper,　a　direct　adaptive　neural　network　control　(DANNC)　method　is　developed　to　deal　with　the　multi-variable　(dissolved　oxygen　concentration　and　nitrate　concentration)　tracking　control　problem　in　wastewater　treatment　processes　(WWTPs),　which　avoids　the　perplex　issue　of　establishing　the　plant　model　of　WWTP　and　has　the　excellent　adaptive　ability.　The　DANNC　system　is　composed　of　neural　controller　and　compensation　controller.　The　neural　controller　is　employed　to　approximate　an　ideal　control　law,　and　the　compensation　controller　is　designed　to　offset　the　network　approximation　error.　The　controller　parameters＇　adaptive　laws　are　deduced　by　the　Lyapunov　theorem.　Simulation　results,　based　on　the　international　benchmark　simulation　model　No.1　(BSM1),　show　that　the　control　accuracy　and　dynamic　performance　of　the　DANNC　method　are　improved　nicely.　©　2014　IEEE.