Objective.　To　establish　an　early　diagnostic　system　for　hypoxic　ischemic　encephalopathy　(HIE)　in　newborns　based　on　artificial　neural　networks　and　to　determine　its　feasibility.　Methods.　Based　on　published　research　as　well　as　preliminary　studies　in　our　laboratory,　multiple　noninvasive　indicators　with　high　sensitivity　and　specificity　were　selected　for　the　early　diagnosis　of　HIE　and　employed　in　the　present　study,　which　incorporates　fuzzy　logic　with　artificial　neural　networks.　Results.　The　analysis　of　the　diagnostic　results　from　the　fuzzy　neural　network　experiments　with　140　cases　of　HIE　showed　a　correct　recognition　rate　of　100%　in　all　training　samples　and　a　correct　recognition　rate　of　95%　in　all　the　test　samples,　indicating　a　misdiagnosis　rate　of　5%.　Conclusion.　A　preliminary　model　using　fuzzy　backpropagation　neural　networks　based　on　a　composite　index　of　clinical　indicators　was　established　and　its　accuracy　for　the　early　diagnosis　of　HIE　was　validated.　Therefore,　this　method　provides　a　convenient　tool　for　the　early　clinical　diagnosis　of　HIE.