In the 1980s and at the turn of last century, severe global waves of sovereign defaults occurred in less developed countries. To date, the forecasting and monitoring results of debt crises are still at a preliminary stage, while the issue is at present highly topical. This paper explores whether the application of the Self-organizing map (SOM), a neural network-based visualization tool, facilitates the monitoring of multidimensional financial data. First, this paper presents a SOM model for visual benchmarking and for visual analysis of the evolution of debt crisis indicators. Second, the method pairs the SOM with a geospatial dimension by mapping the `probability' of a crisis on a geographic map. This paper demonstrates that the SOM is a feasible tool for monitoring indicators of sovereign defaults.