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Aims: This study aimed to simplify fetal monitoring, reduce inter-observer differences and false-positive diagnosis and monitor a large number of births simultaneously. Methods: Fetal signals from several births were transmitted to a central computer via local area network (LAN) or telemetry and analyzed using a multichannel timesharing system. Fetal heart rate (FHR) abnormalities were detected by using three programs: the experts' knowledge system, power spectral analysis and artificial neural network. Abnormal results were automatically communicated directly to the attending doctor. Instead of an FHR chart recorder, the original fetal signals were stored on the computer and re-processed on demand. Results: a maximal FHR score in the first stage of labor indicated a low Apgar score, and correlated with umbilical blood pH. The fetal distress index derived from the FHR score was three or more in cases of fetal acidosis. The neural network yielded probabilities of fetal outcome that coincided with the FHR score, and the neural index derived from these probabilities predicted fetal outcome. Pathological sinusoidal FHR and severe loss of FHR variability were automatically diagnosed by power spectral analysis. Perinatal mortality was 1.1 in 1.000 births, which was significantly lower using this central computerized system than the previous system, and no cases of cerebral palsy were reported 2 months after delivery. Conclusion: The central computerized automated fetal monitoring system improved fetal outcomes even in institutions dealing with a large number of births.