The toolkit for multivariate analysis, TMVA, provides a large set of advanced multivariate analysis techniques for signal/background classification and regression problems. These techniques are embedded in a framework capable of handling input data preprocessing and the evaluation of the results, thus providing a simple and convenient tool for multivariate techniques. The analysis techniques implemented in TMVA can be easily invoked and the direct comparison of their performance allows the user to choose the most appropriate for a particular data analysis. This talk presents recently developed features, such as improved preprocessing, option tuning and an extended unit test framework to ensure code stability. We also discuss the performance of our most important multivariate techniques on example data and a comparison with theoretical performance limits.