Nowadays, one of the main objectives of the companies is based in the generation of monitoring and control systems which ensure the proper function of the processes, resulting in the reduction of costs occasioned by faults on the devices. In order to provide the companies the tools to achieve these objectives, the following work presents a methodology for fault detection and diagnosis in squirrel cage induction motors, with the use of the techniques Motor Square Current Signature Analysis (MSCSA) and fuzzy logic systems. The project covers the following topics: Development of the work bench for fault simulation and load system. Development of the interface for analysis and data processing. Development of the system for fault detection and diagnosis. The system was developed for the detection of 3 faults; Mechanical faults generated by eccentricities (static and dynamic) and electrical faults generated by broken bars in the rotor and / or voltage unbalance. Finally, in order to show the efficiency of the proposed methodology, the project was tested and validated on 2 squirrel cage induction motors of 1 and 3 hp, respectively.