In this paper a generic Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) is proposed. A new knowledge representation scheme in a rule-base is proposed using a belief structure and fuzzy set theory. In this scheme, a rule-base is designed on the basis of the belief structure with belief degrees embedded in ail possible consequents to capture vagueness, incompleteness and nonlinear causal relationships, whilst traditional IF-THEN rules can be represented as a special case. In an established rule-base, an input to an antecedent attribute is transformed into a belief distribution. Subsequently, inference in such a rule-base is implemented using the evidential reasoning approach. The scheme is further extended to inference in hierarchical rule bases. A numerical study is provided to illustrate the potential applications of the proposed methodology.