Countries with high debt exposure are vulnerable to economic and financial shocks that could lead to sovereign defaults. This paper develops a methodology to identify countries that are at risk of debt default based on four elements of debt vulnerability. These elements capture the different ways in which risks associated with high debt are assessed, namely: (i) the fundamental, (ii) the subjective, (iii) the judgmental, and (iv) the theoretical. The fundamental element considers the liquidity, solvency, and institutional risk elements of debt vulnerability. The subjective element captures the investors’ perceptions of debt default, while the judgmental element is based on the debt thresholds as defined by Debt Sustainability Frameworks. Finally, the theoretical element is normative and captures what ought to be. The methodology constructs an index for each of these four elements and uses them as predictors in a model of public debt default. The methodology flags countries that are at risk of default by means of machine learning techniques and delivers outputs that point to underlying causes of vulnerability. The methodology complements existing monitoring tools for assessing debt sustainability.