The volume of water resources data in the world today is increasing at a phenomenal rate. Typically, different aspects of water resources are studied separately and the results combined using heuristics, hierarchy, categorization, zoning or abstract principles for a holistic outlook. The alternative is a simultaneous study of all aspects, which has some advantages. Due to need to handle different types and enormous volume of data in this approach, data mining techniques are required. Data mining looks for patterns, associations and links that lie hidden in the vast amount of data collected on various aspects of a water resources system. The common data mining strategies are: Classification, Clustering, Association, and Estimation. The common data mining tools can be classified under five broad groups of algorithms, which are: function estimation-based, lazy learning-based, meta learning- based, probability-based, and tree-based. These are illustrated with the IRIS data available at UCI data repository.