In this paper, the method of combining the time-domain analysis of empirical mode decomposition (EMD) and fuzzy clustering is explored for the hoist gearbox fault diagnosis. Firstly, it adopts the EMD technique to decompose the signal of vibration. With it, any complicated dataset can be decomposed into a finite and often small number of intrinsic mode functions (IMFs). Then a number of IMFs containing main fault information were selected, from which time domain feature parameters-- variance and kurtosis coefficient were extracted. At last, fuzzy clustering is used to diagnose and identify the kind of fault. The numerical simulation and the analysis of the response signal data from the hoist gearbox show that the method is effective at discriminating the three condition of the gear, i.e. the normal, surface fatigue pitting and cracked tooth.