Dissolved-gas analysis (DGA) is widely used for transformer condition screening and assessment. Conventional DGA practice is to employ statistically derived limits for combustible gas concentrations and their increments or rates of increase for the purpose of classifying a transformer's condition as acceptable, suspicious, or abnormal. The DGA condition assessment, in the form of numeric "condition codes," is sometimes used as part of a transformer health index for prioritizing of testing and maintenance or for asset management functions such as replacement planning. The basis of this condition classification scheme is the seemingly reasonable assumption that higher fault gas levels must represent progressively worsened states of the transformer, with correspondingly reduced reliability. We show that this assumption is not supported by the data. Methods of reliability engineering statistics were applied to a large DGA database augmented with transformer failure data to obtain nonparametric and parametric models of survival probability as a function of a thermodynamic index of fault energy-the Normalized Energy Intensity (NEI), which is based on concentrations of hydrocarbon gases dissolved in the transformer oil. Key concepts from the Probability of Failure in Service (PFS) method devised by Duval and others were adapted for the application of reliability statistics to DGA. The best-fitting parametric model of a pre-failure values distribution for NEI and also for fault gas concentrations is lognormal. From the shape of the associated failure rate curves it follows that-except at low gas concentrations-the instantaneous failure rate relative to NEI actually decreases as NEI increases, implying that higher NEI levels do not imply more impaired transformer reliability. Therefore, "condition codes" based on NEI are not a good basis for classifying transformer health. This conclusion was verified for hydrocarbon gas concentrations as well. While levels of NEI or fault gas concentrations in themselves cannot say whether a transformer's suitability for continued service has been compromised, the survival models show that fault severity associated with a gassing event (producing an increase in NEI over two or more consecutive samples) can be quantified in terms of failure probability, optionally multiplied by a criticality or cost factor. DGA case histories are presented to show how this approach works in practice. KEYWORDS Transformer-DGA-Severity assessment-NEI-Reliability-Risk exposure 1 email@example.com CIGRÉ-807 2016 CIGRÉ Canada Conference, Vancouver, Canada, October 2016.