Abstract: Data-based machine learning covers a wide range of topics from pattern recognition to function regression and density estimation. Most of the existing methods are based on traditional statistics, which provides conclusion only for the situation where sample size is tending to infinity. So they may not work in practical cases of limited samples. Statistical Learning Theory or SLT is a small-sample statistics by Vapnik et al. , which concerns mainly the statistic principles when samples are limited, especially the properties of learning procedure in such cases. SLT provides us a new framework for the general learning problem, and a novel powerful learning method called Support Vector Machine or SVM, which can solve small-sample learning problems better. It is believed that the study of SLT and SVM is becoming a new hot area in the field of machine learning. This review introduces the basic ideas of SLT and SVM, their major characteristics and some current research trends.