Fuzzy set theory and rough set theory are useful mathematical tools for dealing with complex information in many real-world applications. In this paper we describe three aspects of this field: theoretical research into the properties of fuzzy sets and rough sets, research on the efficient implementation of this theory(attribute reduction, rule generation), and finally the development of hybrid systems that combine fuzzy sets or rough sets with other soft computing techniques such as neural networks and genetic algorithms. Hybrid algorithms can greatly improve the quality of the reconstructed system, bringing a much simpler and better solution to many practical applications.