Purpose <p>This study aims to develop a structured methodology for selecting and ranking frequency-domain vibration features to assess the severity of failure in spur gearboxes, taking into account the effects of sensor position and inclination.</p> Methods <p>Vibration signals were acquired from six accelerometers installed with varying spatial positions and inclinations on the gearbox housing. The signals were transformed into the frequency domain via Fast Fourier Transform (FFT), from which 25 condition indicators (CIs) were extracted. A two-step feature ranking strategy was implemented using effect size (Cohen’s d) and classification performance from Random Forest (RF) and K-Nearest Neighbors (KNN). Subsequently, Mahalanobis distance was employed to assess multivariate separation, and factorial ANOVA was used to evaluate the impact of sensor placement and inclination.</p> Results <p>The Third Moment, Fourth Moment, and Central Frequency emerged as the most discriminative features across different fault types and severity levels. Mahalanobis distance increased with fault progression, confirming the effectiveness of the selected features for degradation tracking. Although sensor position and inclination introduced some variability, their influence on classification accuracy was limited.</p> Conclusions <p>The proposed methodology enables interpretable and robust feature selection forcondition monitoring of gearboxes. It enhances diagnostic reliability while demonstrating resilience to sensor placement variability, making it suitable for practical industrial implementations.</p>