We consider the problem of accuracy in heat rate estimations from articial neural network (ANN) models of
heat exchangers used for refrigeration applications. Limited experimental measurements from a manufacturer are
used to show the capability of the neural network technique in modeling the heat transfer phenomena in these
systems. A well-trained network correlates the data with errors of the same order as the uncertainty of the
measurements. It is also shown that the number and distribution of the training data are linked to the performance
of the network when estimating the heat rates under di€erent operating conditions, and that networks trained from
few tests may give large errors. A methodology based on the cross-validation technique is presented to nd regions
where not enough data are available to construct a reliable neural network. The results from three tests show that
the proposed methodology gives an upper bound of the estimated error in the heat rates. The procedure outlined
here can also help the manufacturer to nd where new measurements are needed.