Abstract Capacitive voltage transformers (CVTs) provide instrument-level voltage signals to meters and protective relays in high voltage (HV) systems. The transients in CVTs could lead to protective relay mal-operation. This paper proposes the use of an artificial neural network (ANN) scheme to correct CVT secondary waveform distortions due to CVT transient. This is accomplished by using samples of voltage signals to achieve a good approximation of the inverse transfer function of CVTs, thus an accurate estimation of the primary voltages. Simulations are performed and the impacts of different parameters are studied. Performance results show that the proposed scheme is accurate and reliable. The proposed scheme can also be implemented on a digital signal processor board for real-time application.