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Classifying pipe cracks by size from their pulse-echo ultrasonic signal is difficult but highly significant for the defect evaluation required in pipe testing and maintenance decision making. For this thesis, a binary Support Vector Machine (SVM) classifier, which divides pipe cracks into two categories: large and small, was developed using collected ultrasonic signals. To improve the performance of this SVM classifier in terms of reducing test errors, we first combined the Sequential Backward Selection and Sequential Forward Selection schemes for input feature reduction. Secondly, we used the data dependent kernel instead of the Gaussian kernel as the kernel function in the SVM classifier. Thirdly, as it is time-consuming to use the classic grid-search method for parameter selection of SVM, this work proposes a Kernel Fisher Discriminant Ratio (KFD Ratio) which makes it possible to more quickly select parameters for the SVM classifier.