Safety occurrences in the aviation industry are nowadays commonly regarded as the outcome of a complex system. Due to this systemic view on safety airlines pursue to understand this complex, underlying system and aim to proactively act upon the occurrence of these events. The most prevalent implementation of flight safety event detection is however still threshold analysis, which has no such implications. On the other hand, Machine Learning methods have readily proven to be an efficient and valuable solutions in predicting the occurrence of anomalies in data, such as flight safety events. However, existing methods search for anomalies in datasets encompassing the anomaly, i.e. direct datasets. On the contrary, this study approached airline operations as a complex system which the outcome could be the occurrence of a flight safety event. Hence, the question was raised whether a set of indirect precursors could be significant in predicting flight safety events. That is why common airline processes were selected, in consultation with industry experts, and their indirect data considered. The aim of this study was to evaluate this concept by evaluating a set of precursors for a particular flight safety event (a case study). The Knowledge Discovery in Databases framework was the general guideline throughout this research, with a Relief and Neural Network algorithm as transformation and data mining step respectively. This study showed that the considered processes were significant in predicting the occurrence of a safety event, although the found precursors could not fully encompass the event under investigation. The classification performance of the methodology was characterised by a large number of false positives, which originated from the problem's class skewness. The Matthews Correlation Coefficient proved to be a well-balanced optimisation objective for such problems and overcame this drift. Locally, the weight optimisation showed a set of confidently classified false positives and negatives confined further improvement. These misclassifications were found to be the result of a lack of adequate information. Nevertheless, the considered information did display to be significant as the obtained Matthews Correlation Coefficient and recall underpinned, particularly in the light of the class imbalance and the anomalous nature of flight safety events.