The main objective of this paper is to try to develop statistically and chemically rational models for bromate formation by ozonation of clarified surface waters. The results presented here show that bromate formation by ozonation of natural waters in drinking water treatment is directly proportional to the "Ct" value ("Ctau" in this study). Moreover, this proportionality strongly depends on many parameters: increasing of pH, temperature and bromide level leading to an increase of bromate formation; ammonia and dissolved organic carbon concentrations causing a reverse effect. Taking into account limitation of theoretical modeling, we proposed to predict bromate formation by stochastic simulations (multi-linear regression and artificial neural networks methods) from 40 experiments (BrO(3)(-) vs. "Ctau") carried out with three sand filtered waters sampled on three different waterworks. With seven selected variables we used a simple architecture of neural networks, optimized by "neural connection" of SPSS Inc./Recognition Inc. The bromate modeling by artificial neural networks gives better result than multi-linear regression. The artificial neural networks model allowed us classifying variables by decreasing order of influence (for the studied cases in our variables scale): "Ctau", [N-NH(4)(+)], [Br(-)], pH, temperature, DOC, alkalinity.