This paper introduces an efficient modification of the Rprop algorithm for training neural networks. The convergence of the new algorithm can be justified theoretically, and its performance is investigated empirically through simulation experiments using some pattern classification benchmarks. Numerical evidence shows that the algorithm exhibits improved learning speed in all cases, and compares favorably against the Rprop and a recently proposed modification, the iRprop. 1