The study defines an intelligent neurofuzzy system for antepartum fetal evaluation. The task is to investigate the Doppler ultrasound measurements of umbilical artery (UA) and cerebral artery (CA) to relate the health conditions of fetuses. We, thus, use the UA bloodflow velocity waveforms (FVW): pulsality index (PI), resistance index (RI) and systolic/diastolic ratio (S/D) and the ratios of cerebral-umbilical resistance indices (CRI/URI) in terms of weeks (week index: WI as a normalized value). We, then, make a decision on the basis of fuzzy-rule based system combined with data-based learning strategies such as radial basis function network (RBF) and multilayer perceptron (MLP) for assessing the hypoxia suspicion. A fuzzy grade of membership is used for the evaluation of the seriousness of the situation of the fetus and the diagnostic interpretations for doctors such as good, suspicious and alarming conditions of fetus are derived. With the Doppler indices and fuzzy-rule based interpretation, a specificity and sensitivity of 100% and 98% with RBF, and 100% and 93% with MLP based neurofuzzy technique are obtained, The results prove that intelligent data analysis methods are supportive medical tools for phycisians during intensive surveilance of fetuses.