The thesis outlines the usual parametric analysis of field failure time data for repairable equipments. Due to shortcomings of this black-box approach, exploratory reliability analysis has been adopted to exploit the available data and so learn more about the physical failure process. Elements of exploratory analysis have appeared in recent statistical applications of point process, time series and multivariate methods in the area. These approaches are reviewed and investigated. Exploratory analysis of much field time between failure and limited repair time data for hardware equipments has been undertaken. Despite being from different physical mechanisms, software failure interval data has the same underlying statistical point process as such hardware data and has been similarly investigated. Simple graphs, often with simulation bounds, inference procedures for nonhomogeneous Poisson processes and Box-Jenkins analysis have been used to search for and model aspects of structure expected in reliability data. The appropriateness of the methods is discussed. As well as revealing that (constant) failure rates are often unsuitable summaries, exploratory analysis has highlighted features previously unknown or ignored. The identified time structures, data irregularities and other complexities are described. Exploratory analysis indicated potential dependent failures. A simulation-based graphical tool for highlighting these important events is described. Applications to real data have shown this is a promising approach. Principal coordinates and cluster analyses have been used to explore multivariate field data for automatic fire detection systems in an attempt to identify circumstances leading to false alarms. Data problems limited this analysis. Exploratory analysis has revealed it is common in reliability to assume a too simplistic model formulation compared with the true complex data structures. The implications of this for reliability data collection. storage and analysis are discussed. While an exploratory approach is generally successful, some specialisation of standard statistical methods for reliability is desirable.