The increase in the use of wood in general, and pellets in particular, for individual heating requires attention to be paid to optimizing the combustion of these pellets, particularly in terms of pollutant emissions such as carbon monoxide (CO). This quest for optimization is complicated by the intrinsic variability of wood and, therefore, pellets. In this context, this paper proposes building a neural network model to predict the CO content in smoke based on pellet characteristics and stoves characteristics and settings. However, experiments are costly and time-consuming, which limits the size of the available dataset. In this context, we propose a methodology aimed at training a multilayer perceptron using a small dataset while reducing the risk of overfitting. This methodology is based on finding the minimal network structure and using a robust learning algorithm. The results show that the robust algorithm effectively limits the risk of overfitting and that the final model retains its generalization capabilities despite the small size of the dataset.