This　paper　presents　a　repair　algorithm　for　the　design　of　a　Radial　Basis　Function　(RBF)　neural　network.　The　proposed　repair　RBF　(RRBF)　algorithm　starts　from　a　single　prototype　randomly　initialized　in　the　feature　space.　The　algorithm　has　two　main　phases:　an　architecture　learning　phase　and　a　parameter　adjustment　phase.　The　architecture　learning　phase　uses　a　repair　strategy　based　on　a　sensitivity　analysis　(SA)　of　the　network＇s　output　to　judge　when　and　where　hidden　nodes　should　be　added　to　the　network.　New　nodes　are　added　to　repair　the　architecture　when　the　prototype　does　not　meet　the　requirements.　The　parameter　adjustment　phase　uses　an　adjustment　strategy　where　the　capabilities　of　the　network　are　improved　by　modifying　all　the　weights.　The　algorithm　is　applied　to　two　application　areas:　approximating　a　non-linear　function,　and　modeling　the　key　parameter,　chemical　oxygen　demand　(COD)　used　in　the　waste　water　treatment　process.　The　results　of　simulation　show　that　the　algorithm　provides　an　efficient　solution　to　both　problems.