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Statistical moments were evaluated as suitable parameters for describing swelling and erosion processes (along with drug release) in hydrophilic controlled release matrix tablets. The effect of four independent formulation variables, corresponding to the quantity of four polymeric matrix excipients (namely polyethylene glycol, povidone, and two grades of hydroxyl-propylmethyl cellulose) on statistical moments describing swelling (mean swelling time, MST), erosion (mean erosion time, MET) and drug-release (mean dissolution time, MDT) was evaluated with the aid of multi-linear regression (MLR) and artificial neural networks (ANNs) based on a central composite experimental design. Results were compared to conventional model fitting, where the rate of water uptake during swelling (a), the maximum % water uptake (Smax), the time at which Smax is achieved (tmax), the constant of apparent matrix-tablet erosion rate (ke) and the release exponent (n) from Korsmeyer-Peppas drug-release equation were used as model parameters. Fitting to an external validation test set revealed superior prediction efficacy for statistical moments compared to conventional model fitting, while the combination of statistical moments with ANNs presented the most efficient approach (R2 and RMSEp values of 0.922, 0.833, 0.987 and 0.443, 0.691, 0.173 for MST, MET, and MDT, respectively).