(eng): We study the ability of artificial neural networks to price the European style call and put options on the S&P 500 index covering the daily data for the period from June 2004 to June 2007. We divide the data set into several categories according to moneyness and time to maturity. We then price all options within the categories. The results show that neural networks outperform benchmark ad hoc Black-Scholes model with significantly lower pricing errors across all categories for both call and put options. Moreover, the differences between ad hoc Black-Scholes and neural networks errors widen with deepness of moneyness or longer time to maturity. We show that neural networks, even without the volatility input, can correct for the Black-Scholes maturity and moneyness bias.