A　feedforward　neural　network　modeling　and　control　(FNNMC)　method　is　proposed,　and　its　application　system　is　designed　for　controlling　the　dissolved　oxygen　(DO)　concentration　in　wastewater　treatment　process.　The　convergence　of　the　learning　algorithm　and　the　stability　of　the　feedforward　neural　network　modeling　and　control　system　are　proved　based　on　the　analysis　of　the　learning　rates　of　hidden　layers　in　both　controller　neural　network　and　modeling　neural　network.　In　applying　this　method　to　the　Benchmark　Simulation　Model　No.1　(BSM1),　the　simulation　results　reveal　the　importance　of　properly　selecting　the　learning　rates.　Comparing　with　other　control　methods　such　as　PID　control　method　and　model　predictive　control　(MPC)　method,　we　find　that　this　method　provides　for　the　control　process　of　DO　concentration　with　desirable　modeling　ability　and　high　control　precision　in　steady-state　as　well　as　transient　state.