The non-parametric modelization of the stock options and other derivatives generated an increased interest over the past years. The goal of this paper is to predict the market price of an option from the same information as needed by the Black-Scholes formula. This is a continuation of more recent papers based on the modelization of these prices by the use of neural networks with a structure inspired by our economic knowledge of option pricing. Our contribution, with this paper, is the successful use of the stacking algorithm to improve the generalization of these models. This algorithm combines two training levels for the models, the second being used to improve the out-of-sample deficits of the first one. The obtained results are very interesting, and span the call options of the S&P 500 between 1987 and 1993.