A neuro-computing approach was used for modeling two residence time distribution (RTD) functions — the time-specific (E-type distribution) and the cumulative particle concentration function (F-type distribution) — of carrot cubes in starch solutions in a vertical scraped surface heat exchanger (SSHE) of a pilot scale aseptic processing system. Experimental data obtained for E (t) and F(t) under various test conditions were used for both training and evaluation. Multi-layered artificial neural network (ANN) models with four input and two output neurons were trained. The network was optimized by the varying number of hidden layers, number of neurons in each hidden layer and learning runs, and a combination of learning rule and transfer functions, using a backpropagation algorithm. The trained ANN model was validated by a set of independent experimental data. The ANN models were also compared with conventional models developed based on multiple regression techniques. The results indicated that there was better agreement between experimental and ANN model predicted values for both E (t) and F (t) functions. The average modeling errors associated with ANN were 5.7 and 3.0%, respectively, for E(t) and F(t), while they were 15.5 and 12.3%, respectively, with the multiple regression models.