Fuzzy systems have been used in maintenance and have achieved successful results, however, there are many fields of application inside the area that haven't been sufficiently explored, such as in this case, the diagnosis of electric motors aging. In this work, the basis of fuzzy systems is reviewed making emphasis to the Mamdani inference model and its application for the diagnosis of electrical motors aging is proposed, with the finality of obtaining an aging coefficient that can be used as a fundamental element in industrial maintenance. For the antecedent part are considered as principal variables the temperature, the electric current and the voltage, and for the consequent part the output is the aging coefficient. The system was based in an electric motor which specifications were used to model the system. The knowledge base of the system was extracted from the documentation available, the constant monitory of induction motors and expert's knowledge. The system was applied using a set of hypothetic data to show the system behavior and results showed that the system could be successfully used to represent the human knowledge and benefits of its application are represented with fastest and safest diagnosis, reduction of human errors, improvements in reliability of the motors operation, among others.