Damage due to swelling soil is very noticeable in a wide spectrum of structures such as roads, buildings, canal linings, landfill liners, etc. In order to control or overcome such damage, swelling soils are commonly stabilized either mechanically or chemically. To evaluate the severity of swelling and to design for the best and most economical stabilization strategy, an accurate assessment of the swell potential is required. This report uses a reasonable-sized database representing 413 soils retrieved from 45 different projects covering 28 counties in Kansas to develop prediction models. Neural network-based models and various statistical models were developed and compared for their prediction accuracy. Additionally, the reliability of model predictions were examined using an additional 101 data sets. In the second phase, predictions obtained using the developed neural network models along with the experimental database were used to produce a reliability (probability of success) factor matrix