摘要： A common operational practice in the electric arc furnace (EAF) consist in keeping the electric arc covered with metallic scrap or foaming slag, this is to prevent that the refractory and water cooled panels absorb part of the radiation that should heat the steel. The radiation not absorbed by the steel is not only a waste of energy and money, it is also a latent danger because it damages the walls of the furnace. Today furnace operators use their experience and common sense to predict the degree of coverage of the arc and decide the electrical power the EAF can safely absorb along the process, without damaging the walls and increase the risk of an internal explosion. However, this method is subjective and might leads to human errors. This research was aimed to find robust indicators of arc coverage that could assist the furnace operator in deciding the amount of electrical power the furnace can safely absorb. During this research, it was found a strong relation between the vibrations of the furnace’s shell and the level of coverage of the electric arc. The vibration measurements were done with a laser and the vibration signal was processed using an Artificial Neural Network (ANN) implemented in LabVIEW?. The ANN was trained to emulate the intelligence and knowledge of a very productive and safe furnace operator. Excellent field results were obtained with this implementation and are reported in this paper.