The operation and maintenance of modern sensor-equipped systems such as aircraft generates vast amounts of complex data. Proper use of this data to predict or explain component failures may lead to saving of several thousands of dollars, reducing the number of delays, and increasing the overall level of safety. The field of Knowledge Discovery in Databases (KDD) has delivered a variety of techniques to discover patterns from vast amounts of data. However, none of these techniques are designed to handle the diverse forms of data typically generated during the operation and maintenance of such complex systems. In this research, we study the specific issues to consider during the analysis of commercial aircraft data and propose adequate enhancements of the data mining process to handle these difficulties. The anticipated contributions of this research are related to two fundamental problems in the field of knowledge discovery in databases: i) automatic preparation of the data prior to model development, and ii) use of diverse sources of information.