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The application of four types of process signals in the indirect on-line monitoring of stone hardness has been analysed in this paper. Cutting forces, servomotor currents, vibration and acoustic emission signals were measured during the drilling of three types of stones characterised by different hardness and heterogeneity values. A group of features were extracted from each signal from the time and frequency domain. Their capacity to correctly classify stone hardness was analysed using an artificial neural network classifier. Stone samples were drilled with new drill bits and drill bits worn to three different wear levels in order to analyse the influence of tool wear on the hardness classification process. Nine combinations of cutting parameters were applied for each drill wear level and stone type. Features extracted from the vibration signals obtained the best results in the stone hardness classification. The results indicate their potential industrial application, since they have achieved a high classification precision regardless of the drill bit wear level.

On-Line Workpiece Hardness Monitoring in Stone Machining