SUMMARY Useful lifetime of power cables and related aging failure rates mostly depends on thermal stresses that they experience because of the loss-resulted heat inside the cable in normal or abnormal currents of the network. Knowing the real values of the cable aging failure rate is a key point, and the network operators should enable to estimate the related aging failure rates of their network cables. The already provided methods for cable transient temperature estimation require several different inputs as well as cable installation configuration data that are difficult to collect for operators. Hence, in this article, an artificial neural network-based approach is applied for estimation a cable maximum temperature, which serves a certain daily load curve. The artificial neural network only requires four inputs that are easy to provide. An experimental equation is then used for estimating the cable minimum temperature, and finally, a three-level temperature curve is formed for cable aging failure rate estimation. The life fractions lost during each level of the predicted temperature curve are evaluated by resorting to an already existing combined electrothermal life model held for cable insulation. This method uses the life model and the probabilistic failure model to predict the failure rate of power cables for a future period. The results show that failure rate estimations are in good accordance with exact results, remarking that the estimated three-level stepwise curve of cable temperature is a good approximation of cable thermal transients during cyclic loads. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]