Named Entity Recognition (NER) is a key Natural Language Processing (NLP) task that involves identifying and classifying entities such as people, organizations, locations, and temporal information from text. In this paper, we propose a DistilBERT-based NER system specifically tailored for the aviation domain, where annotated datasets are limited and existing models often underperform. Our dataset, derived from Aviation Herald reports, was annotated in the BIO format and includes entities like aircraft types, airline names, airport codes, and dates. The fine-tuned model demonstrates strong performance, achieving an F1-score of 93% and accuracy of 93.70%. Experimental results show that our approach effectively captures aviation-specific entities and outperforms prior rule-based and hybrid models.