Statistical relational leaming constructs statistical models from relational databases, combining relational learning and statistical learning. Its strong ability and special property make statistical relational learning become one of the important areas in machine learning research. In this paper, the general concepts and characters of statistical relational learning are presented firstly. Then some major branches of this newly emerging field are discussed, including logic and rule-based approaches, frame and object-oriented approaches, functional programming-based approaches. After that several methods of applying rough set in statistical relational learning are described, such as gRS-ILP and VPRSILP. Finally some applications of statistical relational leaning are briefly introduced and some future directions of statistical relational learning and the application of rough set in this area are pointed out.