An essential step in successful mechanized and conventional tunneling operations is a thorough characterization of ground conditions and identification of geological and geotechnical risks. In bored tunneling, this and other information is needed in order to select the appropriate tunnel boring machine (TBM) and to determine its technical specifications for proper soil conditioning and optimal TBM performance. In conventional tunneling, this information is also important for ensuring the correct choice of drilling method and minimizing drilling risk. The major geological risks in tunneling (New Austrian Tunneling Method [NATM] or mechanized) are stickiness and clogging of soils, soil abrasi veness, soils with low fine content or oversized grains, tunnel collapse and instability, groundwater fluctuation, mixed-face conditions, liquefaction, and soil movement. Experiences from both conventional and mechanized tunneling in Iran show that the potential for each geological hazard is higher in certain types of soils. As such, knowledge of the various soil types is important for predicting soil behavior and possible geological hazards. In this study, we present a simple and practical classification of soil types for the prediction of soil behavior and geological hazards in both conventional and mechanized tunneling. With this method, soils are classified based on two parameters. The first is soil grain distribution. For this purpose, tests were conducted on soil samples from the test pit and geotechnical boreholes, and the percentage of particles remaining above a no. 4 sieve and those passing through a no. 200 sieve were calculated for each sample. The second parameter is the consistency index, which is estimated from the Atterberg limits. The soils were classified into ten groups based on these two parameters, and soil behavior and geological hazards for each class were then predicted based on Iranian tunneling experiences. Finally, the real soil behavior and geological hazards in the Qom tunnel were analyzed to determine the accuracy of the predictions. The results showed that the predictions were largely comparable to actual conditions. These results were then directly used for preliminary and permanent lining design and recommendations for soil conditioning, as well as for structural design of the different stations.