Fetal heart rate (FHR) tracing with uterine contraction is the most commonly utilized for fetal monitoring during pregnancy and in the labor. Automated computerized diagnosis of FHR was intended and recently introduced artificial neural network analysis because of its very objective nature. FHR tracing was quantified and FHR score was obtained, FHR frequency power spectrum was analyzed to diagnose sinusoidal FHR to apply in artificial neural network analysis. The neural network was composed of a soft ware with three layers associated with back-propagation system, and it was trained with 8 FHR parameters including the sinusoidal FHR for 10,000 times to obtain 100 % correct internal check. Trained network soft ware was copied with new computer to diagnose new subjects. Diagnostic input was the 8 FHR parameters of 3 periods of 5 min, and output was probabilities to be normal, intermediate and pathologic outcomes in percentage, of which diagnosis was correct in the comparison to simultaneously calculated FHR score that was high in pathologic outcome probability, moderate in intermediate and low in normal outcome probability. Neural network index derived from pathologic and normal outcome probabilities was useful in the outcome prediction of prolonged fetal monitoring.