Due　to　the　vast　deployment　of　object-oriented　software　in　our　day-to-day　livings,　the　issue　of　software　maintainability　prediction.　which　aims　at　ameliorating　the　software　design　process　and　planning　the　amount-constrained　budget　efficiently,　calls　attention　to　it.　In　this　paper,　a　Fuzzy　Neural　Network　(FNN)　based　software　maintainability　prediction　model,　which　combines　the　Artificial　Neural　Network　(ANN)　and　the　Fuzzy　Logic　(FL),　is　proposed.　To　overcome　the　innate　flaws　of　FNN,　a　statistical　technique,　e.g.　Principle　Component　Analysis　(!＇CA),　is　also　used　for　the　sake　of　computational　simplicity.　The　proposed　FNN,　reinforced　by　PCA,　can　effectively　reflect　the　complex　relations　among　independent　and　dependable　variables,　that　is,　by　showing　relatively　high　prediction　accuracy.　The　empirical　experimental　results　verify　this　claim　in　the　sense　that,　with　respect　to　two　disparate　object-oriented　software　data　sets,　the　model　built　by　the　proposed　method　prevails　against　three　other　typical　counterparts,　Multivariable　Linear　Regression　(MLR),　ANN　and　Support　Vector　Regression　(SVR),　in　terms　of　the　prediction　accuracy.