Weathering has several adverse effects on the physical, mechanical and deformation characteristics of rock. However, when determining the weathering degree of rocks, some difficulties are encountered. Ideally, the weathering degree can be determined by simple test results and reliable prediction models. Considering this situation, the purpose of the present study is to construct simple and low cost weathering degree prediction models with two soft computing techniques, artificial neural networks and fuzzy inference systems. When developing these models, model results were tested against data from specimens collected from the Harsit granitoid (NE Turkey) and data published in the literature. Model inputs are porosity, P-wave velocity and uniaxial compressive strength, and model output is weathering degree. The models developed in this study exhibited high prediction performances when checked by train and test data sets. This result shows that the models developed herein can be used for indirect determination of weathering degree. The artificial neural network model requests numerical data as the input, while the fuzzy inference system model can take numerical data and expert opinion as the input. As a conclusion, the models have a high potential when determining weathering degree of a rock for various purposes.