Accuracy　design　constitutes　an　important　role　in　machine　tool　designing.　It　is　used　to　determine　the　permissible　level　of　each　error　parameter　of　a　machine　tool,　so　that　any　criterion　can　be　optimized.　Geometric,　thermal-induced,　and　cutting　force-induced　errors　are　responsible　for　a　large　number　of　comprehensive　errors　of　a　machine　tool.　These　errors　not　only　influence　the　machining　accuracy　but　are　also　of　great　importance　for　accuracy　design　to　be　performed.　The　aim　of　this　paper　is　the　proposal　of　a　general　approach　that　simultaneously　considered　geometric,　thermal-induced,　and　cutting　force-induced　errors,　in　order　for　machine　tool　errors　to　be　allocated.　By　homogeneous　transformation　matrix　(HTM)　application,　a　comprehensive　error　model　was　developed　for　the　machining　accuracy　of　a　machine　tool　to　be　acquired.　In　addition,　a　generalized　radial　basis　function　(RBF)　neural　network　modeling　method　was　used　in　order　for　a　thermal　and　cutting　force-induced　error　model　to　be　established.　Based　on　the　comprehensive　error　model,　the　importance　sampling　method　was　applied　for　the　reliability　and　sensitivity　analysis　of　the　machine　tool　to　be　conducted,　and　two　mathematical　models　were　presented.　The　first　model　predicted　the　reliability　of　the　machine　tool,　whereas　the　second　was　used　to　identify　and　optimize　the　error　parameters　with　larger　effect　on　the　reliability.　The　permissible　level　of　each　geometric　error　parameter　can　therefore　be　determined,　whereas　the　reliability　met　the　design　requirement　and　the　cost　of　this　machining　was　optimized.　An　experiment　was　conducted　on　a　five-axis　machine　tool,　and　the　results　confirmed　the　proposed　approach　being　able　to　display　the　accuracy　design　of　the　machine　tool.