We propose a relatively simple, accurate and flexible approach to forecasting the distribution of defaulted debt recovery outcomes. Our approach is based on mixtures of Gaussian distributions, explicitly conditioned on borrower characteristics, debt instrument characteristics and credit conditions at the time of default. Using Moody's Ultimate Recovery Database, we show that our mixture specification yields more accurate forecasts of ultimate recoveries on portfolios of defaulted loans and bonds on an out-of-sample basis than popular regression-based estimates. Further, the economically interpretable outputs of our model provide a richer characterization of how conditioning variables affect recovery outcomes than competing approaches. The latter benefit is of particular importance in understanding shifts in the relative likelihood of extreme recovery outcomes that tend to be realized more frequently than observations near the distributional mean.