This paper proposes an automatic procedure for condition monitoring. It presents a valuable tool for the maintenance of expensive and spread systems, such as wind turbine farms. Thanks to data-driven signal processing algorithms, the proposed solution is fully automatic for the user. The paper briefly describes all the steps of the processing, from preprocessing of the acquired signal to interpretation of the generated results. It starts with an angular resampling method with speed measurement correction. Then comes a data validation step, in both the time/angular and frequency/order domains. After the preprocessing, the spectral components of the analysed signal are identified and classified into several classes, from sine wave to narrowband components. This spectral peak detection and classification allows the harmonic and side-band series to be extracted, which may be part of the signal spectral content. Moreover, the detected spectral patterns are associated with the characteristic frequencies of the investigated system. Based on the detected side-band series, the full-band demodulation is performed. At each step, the diagnosis features are computed and dynamically tracked, signal by signal. Finally, system health indicators are proposed to conclude the condition of the investigated system. All the steps mentioned create a self-sufficient tool for a robust diagnosis of mechanical faults. The paper presents the performance of the proposed method on real-world signals from a wind turbine drivetrain.