The artificial neural network (ANN) modeling approach was used to predict acrylamide formation and browning ratio (%) in potato chips as influenced by time x temperature covariants. A series of feed-forward type network models with back-propagation training algorithm were developed. Among various network configurations, 4-5-3-2 configuration was found as the best performing network topology. Four neurons in the input layer were reflecting the asparagine concentration, glucose concentration, frying temperature, and frying time. The output layer had two neurons representing acrylamide concentration and browning ratio of potato chips. The ANN modeling approach was shown to successfully predict acrylamide concentration (R = 0.992) and browning ratio (R = 0.997) of potato chips during frying at different temperatures in time-dependent manner for potatoes having different concentrations of asparagine and glucose. It was concluded that ANN modeling is a useful predictive tool which considers only the input and output variables rather than the complex chemistry.