The ability to accurately assess the financial soundness of grain elevators has become an increasingly important task for regulatory authorities. Recent changes in both the general economy and the agricultural economy during the early and mid-1980s have created financial stress in parts of the grain sector. These changes and the resulting increases in the number of grain elevator insolvencies have caused regulators to seek improved means to judge the financial health of grain elevators. The purpose of this study was to develop an accurate and reliable early warning model to assist regulatory authorities in identifying financially troubled country grain elevators in the United States;Three early warning models were estimated and tested in this study. Two models were based on discriminant functions; one linear and the other quadratic. The third model was based on a logistic cumulative distribution function. The purpose of the models was to forewarn regulatory authorities of impending grain elevator insolvency one year in advance;Five variables were hypothesized to be important indicators of the financial health of grain elevators and were included in each of the early warning models developed in this study. The variables were constructed from basic information contained in the elevators' financial statements. Each of the variables included in the models measured a different dimension of firm performance; liquidity, financial structure, cash flow, productivity, and profitability;The findings of this study indicated that: (1) each of the early warning models did a very credible job of distinguishing solvent grain elevators from insolvent grain elevators,(2) the independent variables used were capable of providing information that could discern healthy grain elevators from those likely to fail, (3) the classification performance of the early warning models varied considerably over the range of cutoff scores used for classification, and (4) the early warning model based on the logistic cumulative distribution function generally outperformed the other two models for purposes of detecting grain elevator insolvencies.