Fuzzy objective information systems (FOISs) exist in many applications, and knowledge reduction in them can抰 be implemented by reduction methods in Pawlak information systems. This paper firstly presents new reduction methods including a distribution, a maximum distribution, a assignment, and rough distribution reductions. It then probes into their properties and the relation between them and the reduction methods on Pawlak information systems. Furthermore, this paper proposes the judgement theorems and discernibility matrixes with respect to these reductions. These reductions extend the corresponding methods in Pawlak information systems and provide a new and low computation complexity way for knowledge discovery and rough-fuzzy rule based fuzzy concept classifiers in fuzzy objective information systems.