This study demonstrates the feasibility of utilizing machine learning (ML) for routine identification of sand particles. Identifying different types of sand is necessary for various geotechnical exploration projects because understanding the specific sand type plays an important role in estimating the physical and mechanical properties of the soil. To accomplish this, dynamic image analysis was employed to generate a substantial volume of sand particle images. Individual size and shape descriptors were automatically extracted from each particle image. The analysis involved use of 40,000 binary particle images representing 20 different sand types, and a corresponding six size and four shape descriptors for each particle (400,000 parameters). Six ML models were trained and tested. The work demonstrates that using size and shape features the models efficiently identified up to 49% of individual sand particles. However, when clusters of particles were considered in conjunction with a voting algorithm, classification accuracy significantly improved to 90%. Among the ML models studied, neural networks performed the best, while decision tree exhibited the lowest accuracy. Finally, the use of size consistently outperformed shape as a classification parameter but combining size and shape parameters yielded superior results across all sands and classifiers. These findings suggest that ML holds much promise for automating sand classification using ordinary images.