Recording of maternal uterine pressure (UP) and fetal heart rate (FHR) during labor and delivery is a procedure referred to as cardiotocography. We modeled this signal pair as an input-output system using a system identification approach to estimate their dynamic relation in terms of an impulse response function. We also modeled FHR baseline with a linear fit and FHR variability unrelated to UP using the power spectral density, computed from an auto-regressive model. Using a perinatal database of normal and pathological cases, we trained support-vector-machine classifiers with feature sets from these models. We used the classification in a detection process. We obtained the best results with a detector that combined the decisions of classifiers using both feature sets. It detected half of the pathological cases, with very few false positives (7.5%), 1 h and 40 min before delivery. This would leave sufficient time for an appropriate clinical response. These results clearly demonstrate the utility of our method for the early detection of cases needing clinical intervention.