Inputs for ANN (multihidden-layer feed-forward artificial neural network) models were drying time (t(i + 1)), initial temperature (T0), moisture content (MC0), microwave power, and vacuum pressure. The outputs were temperature (T(i + 1)) and moisture content (MC(i + 1)) at a given t(i + 1). After training the ANN models with experimental data using the Levenberg-Marquardt algorithm, a two-hidden-layer model (25-25) was determined to be the most appropriate model. The mean relative error (MRE) and mean absolute error (MAE) of this model for T(i + 1) were 1.53% and 0.77 degrees C, respectively. In the case of MC(i + 1), the MRE and MAE were 11.48% and 0.04 kg(water)/kg(dry), respectively. Using temperature (T(i)) and moisture content (MC(i)) values at t(i) in the input layer significantly reduced the computation errors such that MRE and MAE for T(i + 1) were 0.35% and 0.18 degrees C, respectively. In contrast, these error values for MC(i + 1) were 1.78% (MRE) and 0.01 kg(water)/kg(dry) (MAE). These results indicate that ANN models were able to recognize relationships between process parameters and product conditions. The model may provide information regarding microwave power and vacuum pressure to prevent thermal damage and improve drying efficiencies.