A Fragility function, which defines the conditional probability of exceeding a limit state given an intensity measure of the earthquake, is an essential ingredient of modern approaches like the performance-based earthquake engineering methodology. However, the generation of such curves generally entails a high computational effort to account for epistemic and aleatory uncertainties associated with structural analysis and seismic load. Moreover, a certain probability function, such as the log-normal distribution, is usually assumed in order to carry out the conditional probability of failure of a structure, without any prior information on the correct probability distribution. In this paper, an artificial neural network model is proposed to carry out fragility curves in order to avoid the aforementioned problems. In this respect, this paper investigates the following aspects: (i) implementation of an efficient algorithm to select proper seismic intensity measures as inputs for artificial neural network, (ii) derivation of surrogate models by using the artificial neural network techniques, (iii) computation of fragility curves by means Monte Carlo Simulations, and (iv) validation phase.