As　geometric　errors　of　rotary　axes　of　a　five-axis　machine　impact　on　its　machining　accuracy,　the　on-machine　measurement　of　rotary　axes　and　their　error　modeling　were　investigated.　Firstly,　the　measurement　method　for　comprehensive　errors　of　rotary　axes　was　presented　based　on　a　standard　ball　and　an　on-machine　measurement　system.　The　measurement　angular　positions　of　the　rotation　axes　were　planned　by　the　random　Hammersely　sequence　and　the　initial　position　of　the　standard　ball　was　determined　with　a　free　installation　strategy.　Then,　a　Radial　Basis　Function　(RBF)　neural　network　model　for　predicting　comprehensive　errors　of　the　rotary　axes　was　built　based　on　subtractive　clustering　and　Fuzzy　C-means(FCM)　cluster.　Finally,　mathematical　analysis　was　carried　out　to　provide　a　new　way　to　model　accurately　for　geometric　errors　of　rotary　axes.　A　cambered　measuring　example　was　used　to　verify　the　proposed　on-machine　measurement　method　and　modeling　method.　The　experimental　results　indicate　that　the　average　deviation　between　the　predicted　points　from　the　mathematics　model　and　measured　points　is　9.6　μm　and　the　maximum　deviation　is　not　more　than　15　μm.　After　the　measuring　results　are　corrected　by　the　mathematic　model　with　a　3D　coordinate　measuring　machine　established　by　this　paper,　the　average　value　is　reduced　to　13.6　μm　from　33.5　μm　and　the　maximum　value　is　reduced　to　18.6　μm　from　62.3　μm.　It　concludes　that　the　measurement　method　is　simple　to　operate　and　has　high　automation.　The　neural　network＇s　training　is　not　only　fast　speed,　adaptable　and　good　robust,　but　also　can　meet　highly　linear　and　strongly　coupling　of　modeling　of　the　geometric　errors　of　rotary　axes.　©　2016,　Science　Press.　All　right　reserved.