In this study a confidence measure for probability density functions (pdfs) is presented. The measure can be used in one-class classification to select a pdf threshold for class inclusion. In addition, confidence information can be used to verify correctness of a decision in a multi-class case where for example the Bayesian decision rule reveals which class is the most probable. Additionally, using confidence values which represent in which quantile of the probability mass a pdf value resides ([0, 1]) - is often straightforward compared to using arbitrarily scaled pdf values. As the main contributions, use of confidence information in classification is described and a method for confidence estimation is presented.