An important problem in questioned document examina- tion is detection of alterations done by inserting words or additional lines of text. In this paper, we present a statistical pattern recognition driven approach that views it as a two- class problem. Given two sample words, one of which is a suspected alteration, it is necessary to determine if the two belong to the same class or different classes. Our approach is defined in two stages. We start with a 11-dimensional vector that comprises colour features defined in HSV space and texture features. During the training phase, we de- rive within-class and between-class L1 distance distribu- tions and identify an optimal threshold that minimizes Type I and Type II errors. During the second or test phase, we take a pair of unkown samples and use the threshold value obtained from the training phase to decide if the two belong to the same class or distinct classes. Our experimental re- sults involving more than 95000 pairs of word images show that the approach gives an accuracy of over 90% for gel and roller pens and an accuracy of 85% for ball pen writings.