The combination of case-based reasoning (CBR) and genetic algorithm (GA) is considered in the problem of failure mode identification in aeronautical component failure analysis. Several implementation issues such as matching attributes selection, similarity measure calculation, weights learning and training evaluation policies are carefully studied. The testing applications illustrate that an accuracy of 74.67% can be achieved with 75 balanced distributed failure cases covering 3 failure modes, and that the resulting learning weight vector can be well applied to the other 2 failure modes, achieving 73.3% of recognition accuracy. It is also proved that its popularizing capability is good to the recognition of even more mixed failure modes.