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Sesame (Sesamum indicum L.) is an important ancient oilseed crop with high oil content (OC) and quality. The direct selection to improve OC of sesame (OCS) due to low heritability leads to a low profit. The OCS modeling and indirect selection through high-heritable characters associated with OCS using advanced modeling techniques is a beneficial approach to overcome this limitation that allows breeder to get a better idea of the plant properties that should be monitored during breeding experiments. This study, carried out in 2013 and 2014, compared the potential of artificial neural network (ANN) and multilinear regression (MLR) to predict OCS in the Imamzadeh Jafar plain of Gachsaran, Iran. Principal component analysis (PCA) and stepwise regression (SWR) were used to evaluate 18 input variables. Based on PCA and SWR, the 6 traits of number of capsules per plant (NCP), number of days from flowering to maturity (NDFM), plant height (PH), thousand seed weight, capsule length, and seed yield were chosen as input variables. The network with the sigmoid axon transfer function and 2 hidden layers was selected as the final ANN model. Results showed that the ANN predicted the OCS with more accuracy and efficacy (R 2 = 0.861, root mean square error [RMSE] = 0.563, and mean absolute error [MAE] = 0.432) compared with the MLR model (R 2 = 0.672, RMSE = 0.742, and MAE = 0.552). These results showed the potential of the ANN as a promising tool to predict OCS with good performance. Based on sensitivity tests, NCP followed by NDFM and PH, respectively, were the most influential factors in predicting OCS in both models. It seems that a breeding program to select or create long sesame genotypes with a long period from flowering to maturity can be a good approach to address OCS in the future. Keywords Artificial neural networks Á Diallel cross Á Multiple regression Á Oil content Á Principal component analysis Á Sesame J Am Oil Chem Soc (2018) 95: 283-297.

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