Water bodies are essential to humans and other forms of life. Identification of such water bodies can be useful in various ways: estimation of water availability, demarcation of flooded regions and so on. In past decades, Landsat sensors have been used for land use classification using various unsupervised and supervise methods. With the introduction of new OLI sensor in Landsat 8 with improved qualities, the accuracy of classification has been much improved. With increasing quality, the data size are also increasing, at the same time data mining techniques are developed to improve the classification efficiencies. The objective of the study is to apply J48 decision tree to identify water bodies using Landsat 8 OLI imageries. J48 is an open source java implementation of C4.5 decision tree. The imagery for the study is from Chuncehon, Republic of Korea area. Training data with individual bands and band ratios were used to develop the decision tree model and later applied to the whole study area. The performance of the result was statically analyzed using Kappa statistics and Area under Curve. The result shows a successful application of data mining technique in robust water body identification.