The wear rate of working tools during the cutting and drilling of rocks is closely related to the abrasiveness of those rocks. As the contact area of the tools increases, due to wear, the specific cutting energy will also increase, and that directly affects the overall consumption of excavation tools. A new artificial intelligence (AI) based model has been developed. It utilizes acoustic emission (AE) and rock properties as main indicators of rock abrasivity, estimated by Cerchar Abrasivity Index (CAI). AE sensors are attached to both the Cerchar testing apparatus and the rock in question while conducting scratch tests using hardened steel pins of 42 and 56 HRC. Prior to the implementation of Artificial Neural Network (ANN) modeling, the selection of independent variables was carried out via Gamma test and V-ratio analyses. As a result, AE parameters, such as total number of events and root mean square of signal, in addition to testing parameters (i.e. uniaxial compressive strength, Young[U+05F3]s Modulus, quartz content and pin hardness) are found to be the optimum model input combination needed to accurately predict CAI.