To improve quality attributes of final dried product and better management of the required energy, optimal process and technology is essential to dry agri-food materials. This work aimed at studying the dehydration characteristics and qualitative traits (color, shrinkage, rehydration ratio) of apple in a rotating-tray convective dryer under different operation variables. Furthermore, to model the dehydration curves, the utility of some well-known semi-theoretical models and artificial neural networks (ANNs) was evaluated. The drying experiments were conducted by practicing constant thickness of the samples (3 mm), different air temperatures (50‒85 °C) and flow rates (1 and 2 m s-1) as well as three tray rotating speeds (0, 6 and 12 rpm). In addition to significant (P < 0.05) reduction caused by increasing the temperature and flow rate, the process duration was considerably decreased by increment in the tray rotating speed. Moisture diffusion inside the slices (2.708×10-9‒8.337×10-9 m2 s-1) was facilitated by increasing the evaluated variables. The average values for activation energy changed from 20.47 to 23.80 kJ mol-1. In comparison with the thin layer models, artificial networks showed better performance in modeling of the curves. Although drying parameters did not significantly affected the studied quality properties, in general, higher drying air velocities and temperatures destroyed the quality of the final products.