It has been shown that Support Vector Machine theory optimizes a smoothness functional hypothesis through kernel application. We present KMOD a two - parameter SVM kernel with distinctive properties of good discrimination between patterns while reserving the data neighborhood information. In classification problems the experiments we carried out on the Breast Cancer benchmark produced better performance than RBF kernel and some stat e of the art classifiers. As well it also generated favorable results when subjected to a 10-class problem of recognizing handwritten digits in th e NIST database .