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(RSM) and the adaptive neuro fuzzy inference system (ANFIS) to optimize oil yield from shea kernels in an hydraulic press. A central composite design (CCD) was adopted to study the effects of five factors namely: moisture content, heating temperature, heating time, applied pressure and pressing time on oil yield. For ANFIS, subtractive clustering method was used in generating the FIS. The experimental data were divided into training and checking data. Cluster centers were evaluated for the training data by competitive learning. RSM suggested that the combination of moisture content and temperature has the most significant effect on the oil yield while ANFISplaced temperature and heating time as the most influential variables. RSM gave a better prediction performance having R2 of 0.9998 while ANFIS has R2 of 0.9865. Themodels developed are useful for the prediction of performance measure, optimization and training for operators.