Computer Aided Detection (CAD) systems are expecting to gain significant importance in terms of reducing the work load of radiologists and enabling the large screening programs. A large share of CAD systems are based on learning from examples, to enables the decision making between the images with or without disease. Images are simplified to numerical descriptors (features vectors) and the system is trained with these features. The common practical problem with CAD systems is training the system with a data from a specific source and testing it on a data from a different source; the variations between sources usually affect the CAD system function. The possible solutions for this problem are (1) normalizing images to make them look more equal, (2) choosing less variation sensitive features and (3) modifying the classifier so that it classifies the data from different sources more accurately. In this project the effect of image variations on the developed CAD system on chest radio graphs for Tuberculosis is studied at Diagnostic Image Analysis Group. Tuberculosis is one of the major healthcare problems in some parts of the world (1.3 million deaths in 2007) [1]. Although the system has a great performance on the train and test data from the same source, using different sub dataset for training and testing the system does not lead to the same result. To limit the effect of image variation of the CAD systems three different approaches are applied for normalizing the images: (1) Simple normalization, (2) local normalization and (3) multi band local normalization. All three approaches enhance the performance of the system in case of various sub datasets for training and testing purposes. According to the improvement achieved by applying normalization it is suggested as a solution for the stated problem above. Although the outcome of this study has satisfactory result, there is always room for further investigations and studies; in specific testing different approaches for finding less variation sensitive features and modifying the classification procedure to a more variation tolerant process.