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The present paper has developed a new multivariate linear regression and adaptive neuro-fuzzy inference processing to predict UCS for weathered granite grade III based on simple input data from point load index, Schmidt rebound hardness, and P wave velocity. Data from 85 rock core samples of this granite type have been selected. By using multivariate regression analysis, three models with two independent variables and one model with three independent variables have been developed. Furthermore, another model has been obtained by using a neuro-fuzzy logic analysis. The root means square error, RMSE, coefficient of determination, R 2 , and the mean absolute percentage error, MAPE, were used as the evaluation criteria of the accuracy of the models. The results have indicated that the regression-based and neuro-fuzzy models are effective, but the accuracy of the neurofuzzy model is in good agreement with the realistic data from the direct test.