This paper presents a low cost reduced instruction set computer (RISC) implementation of an intelligent ultra fast charger for a nickel-cadmium (Ni-Cd) battery. The charger employs a genetic algorithm (GA) trained generalized regression neural network (GRNN) as a key to ultra fast charging while avoiding battery damage. The tradeoff between mean square error (MSE) and the computational burden of the GRNN is addressed. Besides, an efficient technique is proposed for estimation of a radial basis function (RBF) in the GRNN. Hardware realization based upon the techniques is discussed. Experimental results with commercial Ni-Cd batteries reveal that while the proposed charger significantly reduces the charging time, it scarcely deteriorates the battery energy storage capability when compared with the conventional charger. (C) 2007 Elsevier Ltd. All rights reserved.