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Intrapartum electronic fetal monitoring (EFM) is an indispensable means for fetal surveillance. However, the early enthusiasm was followed by scepticism, since the introduction of EFM in every day practise resulted in an increase in operative deliveries. Nevertheless the drawbacks of EFM relate not so much to the technique itself but more to the difficulties in reading and interpreting the fetal heart rate (FHR). In an attempt to develop more objective means to analyse the FHR recordings and compensate for the different levels of expertise among clinicians, computerized systems have been developed during the last 2 decades. In this work, we present an approach to automatic classification of FHR tracings belonging to hypoxic and normal newborns. The classification is performed using a set of parameters extracted from the FHR signal and two hidden Markov models (one for each class). The results are satisfactory indicating that the FHR convey much more information than what is conventionally used.