Pharmacological agents are often used to induce labor. Failed inductions are associated with unnecessarily long waits and greater maternal-fetal risks, as well as higher costs. No reliable models are currently able to predict the induction outcome from common obstetric data (area under the ROC curve (AUC) between 0.6 and 0.7). The aim of this study was to design an early success-predictor system by extracting temporal, spectral, and complexity parameters from the uterine electromyogram (electrohysterogram (EHG)). Different types of feature sets were used to design and train artificial neural networks: Set_1: obstetrical features, Set_2: EHG features, and Set_3: EHG+obstetrical features. Predictor systems were built to classify three scenarios: (1) induced women who reached active phase of labor (APL) vs. women who did not achieve APL (non-APL), (2) APL and vaginal delivery vs. APL and cesarean section delivery, and (3) vaginal vs. cesarean delivery. For Scenario 3, we also proposed 2-step predictor systems consisting of the cascading predictor systems from Scenarios 1 and 2. EHG features outperformed traditional obstetrical features in all the scenarios. Little improvement was obtained by combining them (Set_3). The results show that the EHG can potentially be used to predict successful labor induction and outperforms the traditional obstetric features. Clinical use of this prediction system would help to improve maternal-fetal well-being and optimize hospital resources.