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The development of portable devices that make the reliable diagnosis of faults in electric motors possible has become a challenge for many researchers and maintenance enterprises. These machines intervene in a huge amount of processes and applications and their eventual failure may imply important costs in terms of time and money. However, the aforementioned issue remains unsolved because most of the developed fault diagnosis techniques rely on the user expertise, since they are based on a qualitative interpretation of the results. This complicates the implementation of these methodologies in condition monitoring systems or devices. The objective of this paper is to propose an integral methodology that is able to diagnose the presence of rotor bar failures in an automatic way. The proposed algorithm combines the Discrete Wavelet Transform with the scale transform for feature extraction and correlation coefficient for pattern recognition. The algorithm is applied to both small and large motors operating in a wide range of conditions. The results illustrate the validity and generality of the approach for automatic condition monitoring of electric motors.