This paper reports on advances in identification of relevant features through iterative feature weighting with radial basis function networks. It proceeds with a set of feature weights to scale the data which are used to train a radial basis function network model. Then from the learned model, the feature weights are updated via one-step gradient descent. The updated feature weights are then fed back to build a new model. The procedure continues until we find a satisfactory model and the feature weights converge. Experimental results for some benchmark datasets show that the approach is efficient and effective for selecting relevant features for data modeling and classification tasks.