The particle size distribution (PSD) of dilute latex was estimated through a general regression neural network (GRNN) that was supplied with PSD average diameters derived from multiangle dynamic light scattering (MDLS) measurements. The GRNN was trained with a large set of measurements that were simulated from unimodal normal-logarithmic distributions representing the PSDs of polystyrene (PS) latexes. The proposed method was first tested through three simulated examples involving different PSD shapes, widths, and diameter ranges. Then the GRNN was employed to estimate the PSD of two PS samples; a latex standard of narrow PSD and known nominal diameter, and a latex synthesized in our laboratory. Both samples were also characterized through independent techniques (capillary hydrodynamic fractionation, transmission electron microscopy, and disc centrifugation). For comparison, all examples were solved by numerical inversion of MDLS measurements through a Tikhonov regularization technique. The PSDs estimated by the GRNN gave more accurate results than those obtained through other conventional techniques. The proposed method is a simple, effective, and robust toot for characterizing unimodal PSDs.