In this paper an intelligent approach is introduced to study switching overvoltages during transmission lines energization. In most countries, the main step in the process of power system restoration, following a complete/partial blackout, is energization of primary restorative transmission lines. An artificial neural network (ANN) has been used to evaluate the overvoltages due to transmission lines energization. Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS), were used to train the ANNs. Proposed ANN is trained with equivalent circuit parameters of the network as input parameters; therefore developed ANNs have proper generalization capability. The simulated results for 39-bus New England test system, show that the proposed technique can estimate the peak values and duration of switching overvoltages with acceptable accuracy and EDBD algorithm presents best performance.