Although gearboxes with composite gear trains have been widely used in industrial production, it remains difficult to extract their fault signal features due to relatively complex vibration signals. This paper proposes an effective fault feature extraction method based on ensemble empirical mode decomposition (EEMD) and translation-invariant multiwavelet neighbouring coefficients, through which a clear envelope spectrum of gearbox vibration signals can be obtained. Compared with EEMD denoising or translation-invariant multiwavelet denoising using neighbouring coefficients alone, the method combining both of the denoising approaches can not only effectively suppress the signal noise but also fully retain the fault feature information. The presented method was further experimentally verified using a test rig for a gearbox with a composite gear train. Fault diagnosis was conducted with a single fault, such as snaggletooth and abrasion, as well as mixed faults at different locations. The results have shown that this method can effectively extract the fault features and improve the fault detection rate of a gearbox with a composite gear train. Highlights• This paper proposes a fault feature extraction method based on EEMD and translation-invariant multiwavelet neighbouring coefficients. • This method can be used for gearboxes with composite gear trains. • This method was verified in a test rig with a composite gear train. • The results showed this method is suitable not only for single fault but also for mixed faults.