Audio　classification　is　an　important　tool　used　in　audio　retrieval　and　other　audio　processing　application　areas.　In　this　paper,　five　kinds　of　audio　data　including　speech,　symphony,　jazz,　light　music　and　concerto　are　studied.　Audio　features　are　extracted　by　audio　analysis,　and　formed　into　different　feature　sets.　The　performance　on　applying　Support　Vector　Machine,　Fisher　Kernel　Classifier　and　Potential　Function　Classifier　to　different　audio　feature　sets　is　then　examined.　Finally,　this　paper　presents　the　results　of　a　number　of　experiments,　which　attempt　to　increase　the　classification　accuracy　by　combining　three　classifiers　and　different　feature　sets.　The　classification　accuracy　can　achieve　97%　by　using　a　combination　of　feature　set　1,　set　2,　set　3,　and　set　5　with　Support　Vector　Machine　and　Fisher　Kernel　Classifier,　the　accuracy　is　close　or　better　than　the　ones　reported　on　the　similar　data　sets　and　using　other　classifiers.　©　2010　IEEE.