Lubricating oil plays a critical role in the operation and longevity of internal combustion engines, particularly in diesel-powered urban buses. Monitoring its degradation and contamination offers valuable insights into engine condition, enabling the adoption of Condition-Based Maintenance (CBM) strategies. This study applied multivariate statistical techniques - specifically Principal Component Analysis (PCA) and K-Means clustering - to a dataset of in-service oil samples from a fleet using Lukoil 10W40. The objective was to identify distinct patterns of oil degradation associated with operational conditions and maintenance profiles. Four operational clusters were identified, including: urban-use buses with frequent idling and stop-start cycles; new engines in the break-in phase with high levels of wear metals; mature engines under regular operating conditions; and an outlier bus affected by oil leakage and extreme contamination. The results highlight those conventional indicators like mileage not totally reliable indicators of oil degradation, reinforcing the need for condition-based monitoring using physicochemical and contamination variables.