In this article, a novel feature selection method based on the Fisher ratio (F-ratio) and k-means clustering algorithm is presented and evaluated for nondestructive monitoring of acoustic mission (AE) sources in ship-hull structures. Avoiding complex and time-consuming implementations, the proposed approach use the advantages of the discrimination measure of the F-ratio and the fast convergence rate of a k-means algorithm in the feature selection problem. An extremely efficient set of only four features per sensor is selected for AE sources localization using a radial basis function (RBF) neural network (NN) giving error-free localization accuracy. In the presence of additive white Gaussian noise, different type of information has been selected from the original set of 90 features. Extensive experiments show that even in the very noisy environment of 0dB SNR, a small set of four features can be used for robust neural localization of AE sources giving localization rates better than 94%.