Knowledge maintenance presents a crucial task to preserve high competent Case-Based Reasoning (CBR) systems (a kind of Artificial Intelligent systems for problemsolving), since the accuracy of their offered solutions is strongly dependent on storedcases and their quality. The maintenance aims generally at eliminating two types of undesirable knowledge which are noisy and redundant data. However, inexpedient Case Base Maintenance (CBM) may not only greatly decrease CBR competence in solving new problems, but also reduce its performance in term of retrieval time. Besides, to provide a high maintenance quality, it is necessary to manage uncertainty within knowledge since "real-world data are never perfect" and stored cases within a CBR system’s Case Base (CB) describe real world experiences. To tackle these problems, a research work dealing with maintaining CBs is proposed under the belief function theory framework.