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Industrial processes are being developed under a new scenario based on the digitalization of manufacturing processes. Through this, it is intended to improve the management of resources, decision-making, production costs and production times. Tool control monitoring systems (TCMS) play an important role in the achievement of these objectives. Therefore, it is necessary to develop light and scalable TCMS that can provide information about the tool status using the signals provided by the machine. Due to the lack of this type of systems in industrial environments, this work has two main objectives. First, the predictive capacity of statistical features in the time domain of internal and external signals for the prediction of tool wear in drilling processes was analysed. To this end, a methodology based on automatic learning algorithms was developed. Secondly, once the most sensitive signals to tool wear were identified, algorithms with signals of a certain tool geometry were trained