This paper aims at providing the concept of information granulation in Granular computing based pattern classification that is used to deal with incomplete, unreliable, uncertain knowledge from the view of a dataset. Data Discretization provides us the granules which further can be used to classify the instances. We use Equal width and Equal frequency Discretization as unsupervised ones; Fayyad-Irani's Minimum description length and Kononenko's supervised discretization approaches along with Fuzzy logic, neural network, Support vector machine and their hybrids to develop an efficient granular information processing paradigm. The experimental results show the effectiveness of our approach. We use benchmark datasets in UCI Machine Learning Repository in order to verify the performance of granular computing based approach in comparison with other existing approaches. Finally, we perform statistical significance test for confirming validity of the results obtained. © 2016 IOS Press and the authors. All rights reserved.