Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the helicopter. During health condition mon-itoring, the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear train. According to actual situation, this paper proposed a multi-criteria fusion fea-ture selection algorithm (MCFFSA) to identify an optimal feature subset from the high-dimensional original feature space. In MCFFSA, a fault feature set of multiple domains, including time domain, frequency domain and wavelet domain, is first extracted from the raw vibration data-set. Four targeted criteria are then fused by multi-objective evolutionary algorithm based on decom-position (MOEA/D) to find Proto-efficient subsets, wherein two criteria for measuring diagnostic performance are assessed by sparse Bayesian extreme leing machine (SBELM). Further, F-measure is adopted to identify the optimal feature subset, which was employed for subsequent fault diagnosis. The effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission platform. The experimental results illustrate the superiority of combi-nation of MOEA/D and SBELM in MCFFSA, and comparative analysis demonstrates that the optimal feature subset provided by MCFFSA can achieve a better diagnosis performance than other algorithms.