Risk and safety have always been important considerations in aviation. With the rapid growth in air travel, flight delays, cancellations and incidents/accidents have also dramatically increased in recent years (Nazeri & Jianping, 2002). There is a large amount of knowledge and data accumulation in aviation industry. These data could be stored in the form of pilot reports, maintenance reports, incident reports or delay reports. This paper focuses on different preprocessing and feature selection techniques applied on the 15 component reports of an airline company in Turkey to understand and clean the data set. Regression analysis, anomaly detection analysis, find dependencies and rough sets are used in this study in order to reduce the data set. Also the classification techniques of data mining are used to predict the warning level of the component as the class attribute. For this purpose Polyanalyst, SPSS Clementine, Minitab and Rosetta software tools are used. Find laws module of Polyanalyst is used to find the relations and information retrieval about the components warning level. (C) 2010 Elsevier Ltd. All rights reserved.