In this paper an intelligent approach is proposed to evaluate switching overvoltages caused by shunt reactor energization by applying analytical rules. In a small power system that appears in an early stage of a black start of a power system, an overvoltage could be caused by core saturation on the energization of a reactor with residual flux. An artificial neural network (ANN) has been used to estimate the overvoltages due to shunt reactor energization. Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS), were used to train the ANNs. Equivalent circuit parameters of network have been used as ANN inputs; thus developed ANN is applicable to every studied system. The developed ANN is trained with the worst case of the switching angle and remanent flux, and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can measure the peak values and duration of switching overvoltages with good accuracy and EDBD algorithm presents best performance.