Hydrocyclones are mechanical devices used in classifying and separating many different types of materials. A classification function of the hydrocyclone has been continually developed for solid-liquid separation. In the classification process of solids from liquids, it is desirable to reduce the amount of misplaced material; therefore, the separation sharpness, alpha (alpha), is a parameter that helps in evaluating misplaced material and has been developed as a model to help the designer predict the performance of the classification. However, the problem with the separation sharpness model is that it cannot be used outside the range of conditions under which it was developed. Therefore, this research aimed to develop the separation sharpness model to predict more accurately and cover a wide range of conditions using the multiple linear regression method. The new regression model of separation sharpness was based on a wide range of both experimental and industrial data-sets of 431 tests collaborating with the additional experiments of 117 tests that were obtained from a total of 548 tests. The new model of separation sharpness can be used in the range of 30-762 mm hydrocyclone body diameters and feed solid concentrations in the range of 0.5wt%-80 wt%. When compared with the experimental separation sharpness, the accuracy of the separation sharpness model prediction has an error of 4.53% and R-2 of 0.973. (C) 2019 The Chemical Industry and Engineering Society of China, and Chemical Industry Press Co., Ltd. All rights reserved.