In this presentation, a generic Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER) will be presented. A new knowledge representation scheme in a rule-base is discussed first using a belief structure. In this scheme, a rule-base is designed with belief degrees embedded in all possible consequents of a rule. Such a rule-base is capable of capturing vagueness, incompleteness and nonlinear causal relationships whilst traditional IF-THEN rules can be represented as a special case. Other knowledge representation parameters such as the weights of both attributes and rules are also investigated in the scheme. 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.