We present an algorithm for determining the nature of stochastic processes and their parameters based on the analysis of time series of inertial errors. The algorithm is suitable mainly (but not only) for situations where several stochastic processes are superposed. The proposed approach is based on a recently developed method called the Generalized Method of Wavelet Moments (GMWM), whose estimator was proven to be consistent and asymptotically normally distributed. This method delivers a global selection criterion based on the wavelet variance that can be used to determine the suitability of a candidate model (compared to other models) and apply it to low-cost inertial sensors. By allowing candidate model ranking, this approach enables us to construct an algorithm for automatic model identification and determination. The benefits of this methodology are highlighted by providing practical examples of model selection for two types of MEMS IMUs. Copyright (C) 2015 Institute of Navigation.