Oscillating mills such as OscilloWitt™ (Frewitt) have been widely used in the secondary manufacture of solid dosage forms in the pharmaceutical industry. This type of mill is generally used for moderate milling of difficult-to-process and heat sensitive materials to a particle size range of c.a. 250μm. Particle size distribution is the result of interaction between ribbon properties and process conditions, therefore it is crucial to model and optimize such a complex process in order to produce more uniform particle size distributions. In this work, multiple linear regression (MLR), genetic programming (GP), and artificial Neural Networks (ANN) assisted by 3-fold cross-validation (CV) were used to present generalized models for the prediction of granule size based on the experimental data set. The normalized mean squared error (NRMSE) and the coefficient of determination (R2) for best fit, namely ANN model were obtained as follows: NRMSE=2.28%, R2=0.9926. MLR model was imprecise in the prediction of d10 class. Due to its performance similarities to ANN and its transparency and ease of application, the GP model could be used widely for granule size prediction. Based on the results it was confirmed that the screen size has the most significant effect on the granule size distribution.