Introduction: market baskets, or shopping baskets, are all products that are purchased by a customer at a certain point in time. The analysis of these baskets allows us to know the preferences of our customers, which can be used for various operational, advertising, strategic and logistical purposes. Best of all: it allows us to "predict" their future preferences. We present the case study of an important supermarket chain in the western part of the capital of Chile that needs to obtain key information about their customer market baskets to make decisions. Method: data preprocessing was performed in order to transform the original data into market baskets. We clustered market baskets using artificial neural networks of the self-organizing maps (SOM) class. The use of the algorithm included the search for the best hyperparameters: grid size and learning rate. Results: the result of the best SOM found identifies six clusters of market baskets, each based in one predominant product, and identifies the products most related to them. Conclusions: recommendations on frequent shopping baskets have been made to the supermarket chain that has provided the data used in the research.