This paper presents a method of feature extraction in the context of aviation data analysis. The underlying algorithm utilizes a feature extraction algorithm called symbolic dynamic filtering (SDF) that was recently published. In SDF, time-series data are partitioned for generating symbol sequences that, in turn, construct probabilistic finite state automata (PFSA) to serve as features for pattern classification. The SDF-based algorithm of feature extraction, which enjoys both flexibility of implementation and computational efficiency, is directly applicable to detection, classification, and prediction of anomalies and faults. The results of analysis with real-world flight recorder data show that the SDF-based features can be derived at a desired level of abstraction from the information embedded in the time-series data. The performance of the proposed SDF-based feature extraction is compared with that of standard temporal feature extraction for anomaly detection. Our study on flight recorder data shows that SDF-based features can enable discovering unique anomalous flights and improve the performance of the detection algorithm. We also theoretically show that under certain conditions it may be possible to achive a better or comparable time complexity with SDF based features.