In this work we introduce a nonparametric recursive aggregation process called Multilayer Aggregation (MLA). The name refers to the fact that at each step the results from the previous one are aggregated and thus, before the final result is derived, the initial values are subjected to several layers of aggregation. Most of the conventional aggregation operators, as for instance weighted mean, combine numerical values according to a vector of weights (parameters). Alternatively, the MLA operators apply recursively over the input values a vector of aggregation operators. Consequently, a sort of unsupervised self-tuning aggregation process is induced combining the individual values in a certain fashion determined by the choice of aggregation operators.