Safety is a key strategic management concern for safety-critical industries and management needs new, more efficient tools and methods for more effective management routines. Effective methods are needed to identify and manage risks in both aviation and other safety-critical industries in order to improve safety. Analysing safety related records and learning from “touch and go” situations is one possible way of preventing hazardous conditions from occurring. The eventuality of an incident or an accident may markedly be reduced if the risks connected to it are efficiently diagnosed. With the aid of this outlook, flight safety has witnessed decades of successful improvement. This paper introduces aviation safety data analysis as an important application area for data mining. In this research text mining was utilised to study 1,240 flight safety reports testing three different systems, applying clustering to find similarities between

reports, perhaps containing the indications of a lethal trend, without any presumption of their existence. All the different systems produced coherent results, proving that mining could extract information from unstructured data, which might not be possible with conventional methods.