This study investigates the multi-period prediction of firm bankruptcy as a multi-alternative problem of Statistical Decision Theory. This approach enables a simultaneous assessment to be made of the prediction of bankruptcy and the time horizon at which the bankruptcy could occur. To illustrate the approach, using U.S. bankruptcy data, a comparative statistical analysis of various financial variables is undertaken to identify four relatively independent financial ratios that have the potential for multi -period bankruptcy forecasting. These ratios characterize the quantity and quality of debt, as well as the firm's ability to repay the debt. The study also investigates a new type of predictive information - the maturity schedule of a firm's long-term debt. Bayesian-type forecasting rules are developed that jointly use the financial ratios and maturity schedule factors. The rules noticeably enhance bankruptcy prediction compared with the familiar one-period (two-alternative) Z-score rules of Altman (1968) for bankruptcy within the first one, two or three years. Predictive factors derived from schedule information additionally enhance bankruptcy prediction at distant time horizons.