Currently, network operators are facing a situation in which their high-voltage assets are reaching or even exceeding their design lifetimes [1]-[3]. The problem of future replacement of assets must thus be considered [4], [5]. Spare parts must be available to ensure replacement of components that fail during operation. In practice, utilities adopt two different approaches to assessing the condition of their assets [6], [7], namely bottom-up and top-down analysis. Bottom-up analysis uses aging characteristics of the materials within a given asset, and diagnostic measurements are performed to assess the physical degradation of the various parts of that asset. In contrast, top-down analysis uses mathematics to analyze the service-lifetime data of the whole population under consideration and to estimate the number of future failures within the population. In practice, both approaches have limitations due to differences in component design, operational conditions, environment, and maintenance programs [1]. An additional difficulty arises from ongoing technological improvements, e.g., in the properties of materials used in high-voltage components over a period of perhaps 40 years. In this paper, parametric statistical methods are used to analyze the time to failure of high-voltage components and to estimate the number of future failures. Attention is drawn to several problems that complicate the statistical analysis of service-lifetime data. Detailed information on the basic theory of statistical analysis of failure data can be found in [5], [6], [8]. Using service-lifetime data provided by a Dutch utility, and Monte Carlo simulations, three case studies of the failure of high-voltage components are presented.