Knowledge acquisition is the most important part in the development of expert system. It deals with extracting knowledge from sources of expertise and transferring it to a knowledge base. Knowledge acquisition is major research field in knowledge engineering and still the most difficult and error-prone task for knowledge engineer while building an expert system. This situation influences the performance of the knowledge due to the quality of information and the reduction of error possibility. It is not an easy task to acquire knowledge from human expert not trained in knowledge engineering. The performance of the knowledge is performed by interaction between experts and knowledge engineer or machine during acquisition process. In most rule-based expert system, building of rules can easily be done. Knowledge Engineer or expert does not have to do any work specifying rules and how they are linked to each other. Sometime the knowledge engineer or expert can reference rules or facts that have not yet been created. It seems to be a simple and an instant work. The problem due to the performance of the knowledge will not occur until the number of rules is getting higher. Some problem may appear in the form of inconsistent rules, unreachable rules, redundant rule and rotating chain of rules. In order to solve that problem and to achieve that mentioned performance, a rule-based knowledge acquisition system using Ternary Grid is developed. This system acquires knowledge from human expert using grid or matrix system. Ternary Grid represents a model of rule-based knowledge in a grid or matrix format.