Case-Based Reasoning systems usually retrieve cases using a similarity function based on K-NN or some derivatives. These functions are sensitive to irrelevant or noisy features. Weighting methods are used to extract the most important information present in the knowledge and determine the importance of each feature. However, this knowledge, can also be incorrect, redundant and inconsistent. In order to solve this problem there exist a great number of case reduction techniques in the literature. This paper analyses and justifies the relationship between weighting and case reduction methods, and also analyses their behaviour using different similarity metrics. We have focused this relation on Rough Sets approaches. Several experiments, using different domains from the UCI and our own repository, show that this integration maintain and even improve the performance over a simple CBR system and over case reduction techniques. However, the combined approach produces CBR system decrease if the weighting method declines its performance.