Neural networks have considerable potential for applications in particulate image analysis. An area of great current interest is to use image analysis techniques to characterize particle size distributions in video images of blasted rock. A simulated neural network was trained to recognize fragmented rock size classes taken from images of blasted ore in a large open pit mining operation. Size distributions were assigned to categories such as 40% and 60% minus six inches Pattern recognition features were extracted from digitized images using two-dimensional Fourier transforms. These features were then used as a training set to enable the neural network to recognize the size category of subsequent images of blasted rock taken from the mining operation. Training sets were developed for a back propagation algorithm by hand sorting and sizing the blast fragments from photographed piles. Within the limits of this experiment, the trained network consistently recognized the size distribution categories. A trained neural network can be readily calibrated to adjust for changes in light and shadow, a problem which plagues algorithm-based blast fragmentation analysis routines. Neural network techniques may provide a solution to the problem of rapid and reliable on-line and on-site size distribution recognition and assessment.