A hybrid intelligent system that is able to successively refine knowledge stored in its rulebase is developed. The existing knowledge (referred to as belief rules), which may initially be defined by experts in a particular domain, is stored in the form of rules in the rulebase and is refined by comparing it with new knowledge (referred to as evidence rules) extracted from data sets trained under a neural network. Based on measurement, assessment, and interpretation of rule similarity, belief rules existing in the rulebase may be found to be confirmed, contradicted, or left unsupported by new training data. New evidence rules may also be discovered from the training data set. This rule comparison is unique in the sense that rules are viewed and compared in a geometric manner. As rules evolve in existence in the rulebase during the belief-evidence fusion process, their bounds, strengths, and certainties are also revised. The hybrid intelligent system is tested with different data sets, including hypothetical data sets and actual data sets.