Accurate identification of faults in gearboxes is of vital importance for the safe operation of helicopters. Although hidden Markov models (HMMs) with Gaussian observations have been successfully used for fault diagnostics of mechanical systems, a Gaussian HMM must assume that the observation sequence is generated from a Gaussian process. Conversely, vibration signals from helicopter gearboxes are often non-Gaussian and non-stationary. Also, it always needs to use multi-sensors for more accurate fault diagnostics in practice. Thus, a classical Gaussian HMM may not meet the need of helicopter gearboxes, and it needs to study novel HMMs to model multi-sensor, non-Gaussian signals. This paper presents a multi-sensor mixtured HMM (MSMHMM), which is built on multi-sensor signals. For a MSMHMM, each sensor signal will be considered as the mixture of non-Gaussian sources, so it can depict non-Gaussian observation sequences very well. Then, learning mechanisms of MSMHMM parameters are formulated in detail based on the expectation-maximization (EM) algorithm and a framework of MSMHMM-based fault diagnostics is proposed. In the end, the proposed method is validated on a helicopter gearbox, and the results are very exciting.