A general regression neural network technique was applied to design optimization of a liquid-liquid coaxial swirl injector. Phase Doppler Anemometry measurements were used to train the neural network. A general regression neural network was employed to predict droplet velocity and Sauter mean diameter at any axial or radial position for the operating range of a liquid-liquid coaxial swirl injector. The results predicted by neural network agreed satisfactorily with the experimental data. A general performance map of the liquid-liquid coaxial swirl (LLCS) injector was generated by converting the predicted result to actual fuel/oxidizer ratios.