The Particle Size Distribution (PSD) properties of dam granular material plays an important role in the construction process ofearth-rock dams, as it can affect the filling quality and structural safety. However, the conventional sieving method employed tocheck the PSD is labor-intensive, time-consuming and not highly accurate. In this study, a digital image-based identification methodis presented for the determination of the PSD of dam granular material, which mainly incorporates image acquisition technology, alarge database and a neural network. Digital Image Processing (DIP) technology is used to recognize the geometric size and gradingcurve of dam granular materials at a small scale, while statistical distribution models are used to determine the characteristicparameters of the grading curve and convert the graphical curve into mathematical variables. Furthermore, a large database and a BPneutral algorithm, which is improved using a genetic algorithm, are introduced as tools to reveal the implicit relationship between theDIP and sieving grading curves to correct the error of identification. A case study for the Changheba Hydropower Station is used toillustrate the implementation details of the presented method. The identification results demonstrate that the presented method canacquire and assess the gradation in spite of a degree of error, which can be decreased when more advanced DIP technologies areexplored, the amount of data in the database is increased, and a more optimized network algorithm is adopted.