Prediction of tool wear by monitoring the vibrations generally based onthe concept, where the vibration created during the machining process is incorrelation with the tool wear phenomenon. The prior detection of toolwear phenomenon can provide increase of performance in process of machining. Substantial amount of variations in the vibrations of themachine and tool are acquired through the MPU-6050 sensor and isuploaded to a cloud server. Relationship was established between thespeed, depth of cut, feed rate and vibrations. With all the values recordedfrom the cloud server a machine learning mode was trained to predict the tool wear prior to the occurrence of this phenomenon. These results provideinitial elements towards the implementation of online monitoring and predictive maintenance of the tools and machine.