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The purpose of this work is to introduce one of the most promising among re-cently developed statistical techniques – the support vector machine (SVM) – to corporate bankruptcy analysis. An SVM is implemented for analysing such predictors as financial ratios. A method of adapting it to default probability estimation is proposed. A survey of practically applied methods is given. This work shows that support vector machines are capable of extracting useful infor-mation from financial data, although extensive data sets are required in order to fully utilize their classification power. The support vector machine is a classification method that is based on sta-tistical learning theory. It has already been successfully applied to optical character recognition, early medical diagnostics, and text classification. One application where SVMs outperformed other methods is electric load prediction (EUNITE, 2001), another one is optical character recognition (Vapnik, 1995). SVMs produce better classification results than parametric methods and such a popular and widely used nonparametric technique as neural networks, whic