A new predictive model for evaluating the vibration of a sawing machine was developed using a new rock classification system. The predictors are machine parameters and a rock sawability index. The new rock classification system includes four major parameters of the rock: uniaxial compressive strength, abrasiv- ity index, mean Moh’s hardness, and Young’s modulus. The FAHP approach was used when determining the weights of these parameters by six decision makers. Two groups of carbonate rocks were sawn using a fully-instrumented laboratory sawing rig at different feed rates and depths of cut. During the sawing trials system vibration was monitored as a measure of saw performance. Then, a new statistical model was obtained by multiple regression on the machining parameters and the rock sawability index. The model is very useful for the evaluation of the system vibration, and for selecting suitable machining parameters, from a limited set of mechanical properties. A new predictive model for evaluating the vibration of a sawing machine was developed using a new rock classification system. The new rock classification system includes four major parameters of the rock: uniaxial compressive strength, abrasiv - ity index, mean Moh’s hardness, and Young’s modulus. The FAHP approach was used when determining the weights of these parameters by six decision makers. Two groups of carbonate rocks were sawn using a fully-instrumented laboratory saw rig rig at different feed rates and depths During the sawing trials system vibration was monitored as a measure of saw performance. Then, a new statistical model was obtained by multiple regression on the machining parameters and the rock sawability index. The model is very useful for the evaluation of the system vibration, and for selecting suitable machining parameters, from a limited set of mechanical properties.