There is little process control employed at aggregate crushing plants and essentially no optimization at the primary or jaw crushing stage. Jaw crusher selection is very dependent on the subjective judgment/experience of individuals, the characterization of rock material using inadequate and unrepresentative tests, and the desire to limit secondary breakage, resulting in the conservative selection and operation of jaw crushers. A method for predicting the power consumption, product size, and volumetric capacity of jaw crushers based on fracture toughness has been proposed in this study. A new fracture toughness test, the Edge Notched Disk Wedge Splitting test, has been developed and verified in order to rapidly assess the fracture toughness of six quarry rocks. A High Energy Crushing Test system has been used to simulate the operational settings of a jaw crusher so that comparison of fracture toughness, specific comminution energy, and breakage distribution could be performed. The results indicate that the specific comminution energy required to reduce a rock particle to a given size increases with fracture toughness. The breakage distribution has also been shown to be dependent upon fracture toughness as long as the elastic modulus is taken into account. Laboratory jaw crushing experiments show that the capacity of a jaw crusher is dependent upon fracture toughness and the elastic modulus. Models for the prediction of power consumption, breakage function/product size, and volumetric capacity have been developed based on these results. At the experimental level, the models were able to predict the specific comminution energy to within 1% and t10 (characteristic crushing parameter) to within 10%. Prediction of the product size distribution produced by a lab-scale jaw crusher, for four different rocks, was within ± 5% (in terms of percent passing). The models allow for the selection of a jaw crusher based on the nature of the rock being broken and the average amount of size reduction done on the feed material. The models can also be used to optimize feed and operational settings, as well to determine the product size produced for a given rock and reduction ratio.