The presented work of thesis arises from the desire of the ISI-Welding Company to have a reliable system of predictive maintenance for the dressing of electrodes in welding guns. The promise of predictive maintenance is to allow a convenient scheduling of corrective maintenance and to prevent unexpected equipment failures with the goal to extend product useful life. Machine learning algorithms lend well for this purpose permitting, based on fairly large data sets, to find common relations between the data and to identify a common pattern for a given unsafe situation. The first step has been to investigate the nature of spot welding phenomenon finding both e polynomial and a physical model.