This paper presents an approach to categorizing Bangla language question into some predefined coarse-grained category that represents expected answer type of that particular question. Support vector machine was used with different kernel function to increase the accuracy of existing Bangla question classification system. Both predefined feature set and the stream of unigram based on the frequency of data set was considered to build feature matrix. For five cross validation average 89.14% accuracy was achieved using 380 top frequent words as the feature which outperformed existing single model based Bangla question classification system. For same cross validation, 88.62% accuracy was achieved with a combination of wh-word, wh-word position and question length as feature set.