Experiments are performed to investigate the atomization characteristics of mixed-interaction regions of sprays of two swirl injectors installed side by side. Both droplet size and velocity distributions on a plane perpendicular to the axes of the injectors are measured using a PDA system. As a result of the interaction phenomenon, a region of secondary atomization is identified that differs significantly from the hollow region spray of a single swirl injector. A neural network algorithm is used to reconstruct the entire spray field for both droplet size and velocity distribution in extrapolation regimes for injector spacing as well as three dimensional spatial coordinates. Excellent agreement between the predicted values and the measurements is obtained. It is observed that points on the extrapolation regime of the neural network can be predicted with an accuracy of 93 % using a training data set with less than 50 % of the number of data points to be predicted. The results indicate the capability of performing design- and optimization studies for pressure-swirl injectors, with sufficient accuracy, by applying a modest amount of data in conjunction with an overall optimized value for the width of the probability.