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In a previous paper [1] the authors presented a methodology to estimate the probability of failure of power transformers due to paper insulation degradation. The methodology was based on the identification of patterns in indirect measurements by means of an artificial neural network (ANN). The parameters measured were the amounts of dissolved gases and other chemical in the transformer oil. The failure probability was then estimated from the population life data. The methods presented in this paper are useful to estimate the quantitity of cases required for the training of the ANN to achieve acceptable predicted values, which is particularly important when the available data is limited.