Persistent contrails are a major contributor to the effective radiative forcing from aviation. Optimizing flight trajectories to avoid regions prone to the formation of warming contrails has therefore been proposed as a mitigation strategy to achieve the international climate targets for the aviation sector. However, the precise attribution of observed contrails to individual flights remains a challenge for both the evaluation of avoidance measures and the validation of contrail models. This study presents a fully automated evaluation framework designed for matching contrails to flights using observations from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) instrument aboard the geostationary Meteosat Second Generation (MSG) satellite and flight trajectories. The method utilizes an automated contrail detection algorithm. Although such automated detections designed for MSG/SEVIRI often face a trade-off between high detection efficiency and low false alarm rate, the proposed matching process substantially improves detection reliability through a multi-step verification: contrails are preselected based on their spatial and temporal occurrence as well as their spatial orientation with respect to the flight trajectories, followed by temporal tracking. A new aspect is the development of a life cycle-based confidence score to derive a quantitative matching score. In contrast to previous approaches, the proposed method is entirely observation-driven, requires no model input, and enables rapid, large-scale application. The framework’s performance is demonstrated through two specific case studies. Future applications include the assessment of contrail avoidance trials across the Europe-African airspace and adaptation to other satellite configurations.