Activated　sludge　wastewater　treatment　processes　(WWTPs)　are　difficult　to　control　because　of　their　complex　nonlinear　behavior.　In　this　paper,　an　adaptive　controller　based　on　a　dynamic　structure　neural　network　(ACDSNN)　is　proposed　to　control　the　dissolved　oxygen　(DO)　concentration　in　a　wastewater　treatment　process　(WWTP).　The　proposed　ACDSNN　incorporates　a　structure　variable　feedforward　neural　network　(FNN),　where　the　FNN　can　determine　its　structure　on-line　automatically.　The　structure　of　the　FNN　is　adapted　to　cope　with　changes　in　the　operating　characteristics,　while　the　weight　parameters　are　updated　to　improve　the　accuracy　of　the　controller.　A　particularly　strong　feature　of　this　method　is　that　the　control　accuracy　can　be　maintained　during　adaptation,　and　therefore　the　control　performance　will　not　be　degraded　when　the　character　of　the　model　changes.　The　performance　of　the　proposed　ACDSNN　is　illustrated　with　numerical　simulations　and　is　compared　with　the　fixed　structure　fuzzy　and　FNN　approaches;　it　provides　an　effective　solution　to　the　control　of　the　DO　concentration　in　a　WWTP.　(C)　2011　Elsevier　B.V.　All　rights　reserved.