This study contributes to the credit risk management literature by describing a new, user-friendly, generic neural network-based methodology for developing credit-scoring models for small businesses based on commonly available data. The methodology is used to construct and validate a model employing data from a pool of terminated small business loans made by an economic development lender based in Maine. A total of 138 variables representing loan characteristics are initially examined, and are subsequently reduced to a set of five input variables that are effective predictors of loan default. These variables, which are composed mainly of traditional financial ratios, are then used to build a probabilistic neural network model that correctly predicts the ultimate disposition of 92% of the loans in the out-of-sample testing. These results are better than those of a binary logistic regression model that correctly classified 86% of the loans.