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Alteration and addition to valuable data on paper documents are among the fastest growing crimes around the globe. The loss due to these crimes is huge and is increasing with an alarming rate. The techniques, which are used by forensic document examiners, to examine such cases are still limited to manual examination of physical, chemical and microscopic characteristics. Moreover, it is very difficult to detect an alteration when the ink of similar color is involved. We could not find much in the literature to deal with this problem in an automated pattern recognition framework. In this paper, we restrict ourselves to alterations made with ball-point pen strokes and propose a scheme for detection of such alterations using pattern recognition tools. For this, a large set of color and texture based features is extracted. To choose an adequate set of useful features from the extracted ones, a multilayer perceptron (MLP)-based feature analysis technique is used. For detection of the alteration, three different classifiers, namely, K-nearest neighbor, MLP and support vector machines are used. The results are quite promising.