There are different analytical scenarios assumed for crack propagation in the gear tooth root. This paper presents an investigation of the performance of statistical fault detection indicators (the RMS and kurtosis) for three different series of crack propagation scenarios, to compare these scenarios from a fault diagnostics point of view. These scenarios imply different forms of cracks with propagation by a certain step of crack depth. The first scenario assumes a crack being extended through the whole tooth width with a uniform crack depth distribution, while the second scenario assumes the crack being extended through the whole tooth width with a parabolic crack depth distribution, and finally in the third scenario the crack is assumed to be propagating in both the depth and the length directions simultaneously. The time-varying gear mesh stiffness has been investigated using the programme code developed in the present research, and the crack propagation can be modelled with any of the presented crack propagation scenarios. Dynamic simulation has been performed to obtain the residual signals of all the studied cases for each crack propagation scenario. The comparison of the statistical indicators applied to the residual signals shows that in the first scenario the faults are most easily detectable, since in this scenario there is a change in the indicators implying a dramatic decrease in the gear mesh stiffness. The fault detection in the 2nd scenario is more difficult, as the crack propagates with no significant reflection on the mesh stiffness loss. The 3rd proposed scenario should receive more attention in research because it could occur in reality in case of non-uniform load distribution. However, with this scenario it is difficult to perform early fault detection, since there is a very slight change in the statistical indicators at the beginning of the crack propagation. After which, these indicators show a significant change when the crack grows deeper which implies a serious crack propagation condition. (C) 2013 Elsevier Ltd. All rights reserved.