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The existence of broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor current spectrum. It has been shown that these broken rotor bar-specific frequencies are settled around the fundamental stator current frequency and are termed lower and upper sideband components. Broken rotor bar fault detection schemes should depend on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple discriminant analysis (MDA) provides an appropriate environment to develop such fault detection schemes because of its multi-input processing capabilities. The focus of this paper is to provide a new fault detection methodology for broken rotor bar fault detection and diagnostics in terms of its multiple signature processing feature and the motor operation partitioning concept to improve the overall detection performance. This paper describes two fault detection schemes within this methodology, and demonstrates that multiple signature processing is more efficient than single signature processing. The first scheme, which will be named the "monolith scheme," is based on a single large-scale MDA unit representing the complete operating load torque region of the motor, while the second scheme, which will be named the "partition scheme," consists of many small-scale MDA units, each unit representing a particular load torque operating region.