Several Danish small and medium-sized banks have become distressed during and after the global financial crisis. In this paper, a multiple logistic regression model is used to identify which factors characterize the distressed Danish banks from 2008-12. The factors are chosen from a broad range of variables, i.e. the model is unrestricted. The estimated model identifies the distressed banks fairly well. The variables that altogether best describe the probability of a bank becoming distressed are: a bank's excess capital in per cent of risk weighted assets, the 3 year average lending growth lagged 2 years, property exposure, and a benchmark for stable funding (the so-called funding-ratio). The variables are all adjusted with the sector average to account for the general development during the period. Based on experiences from this and past crises the Danish FSA introduced the so-called Supervisory Diamond as part of its banking supervision in 2010. A multiple logistic regression model is estimated with deviations from limit values set in the supervisory diamond to assess whether the variables in the supervisory diamond differ from the unrestricted model. Overall, the analyses support the establishment of benchmarks. The results of this analysis show that deviations from the benchmarks concerning property exposure and funding-ratio are statistically significant with expected signs. However, deviations from the benchmarks concerning lending growth, large exposures, and excess liquidity cover are statistically insignificant.