In the paper there is demonstrated the impact of the selection of training sample on misclassification error for the case of a real database describing the food market. The methodological background is a decision tree algorithm as it is applied with different rules and parameters (e.g. varying stopping rules and sample sizes). The goodness of the model fit is analysed.