Dissolved　Oxygen　(DO)　is　one　of　the　most　important　parameters　describing　biochemical　process　in　wastewater　treatment.　It　is　usually　measured　with　dissolved　oxygen　meters,　and　currently　galvanic　and　polarographic　electrodes　are　the　predominant　methods.　Expensive,　membrane　surface　inactivation,　and　especially　need　of　cleaning　and　calibrating　very　frequently　are　common　disadvantages　of　electrode-type　measuring　sensors.　In　our　work,　a　novel　method　for　classifying　and　further　measuring　dissolved　oxygen　based-on　image　processing　and　artificial　neural　network　was　researched.　Pictures　of　the　water-body　surface　in　aeration　basins　are　captured　and　transformed　into　HSI　space　data.　These　data　plus　the　correspondent　measured　DO　values　are　processed　with　a　neural　network.　Using　the　well-trained　neural　network,　a　satisfied　result　for　classifying　dissolved　oxygen　according　to　the　water-body　pictures　has　been　realized.　©2009　IEEE.