The remote field eddy current technique is used to inspect conductive pipes and to estimate the dimensions of flaws liable to exist in the conductive material. A data set which contains observations for calibrated flaws is used to learn the processing. This learning problem is addressed in the context of small size data set in which the overfitting problem is often present. To obtain a robust estimation of flaw size, this problem is minimised as following: the estimation of flaw size uses parameters which number is chosen the smallest possible. To obtain this set of parameters, three approaches are proposed. A reduction of the data space dimension by means of principal component analysis and parametric modeling, is carried out. Then, for both cases a bilinear regression is performed to estimate the size flaw. The third approach uses a neural network to learn the processing and to directly calculate an estimate of flaw size. A MDL (Minimum Description Length) criterion is used in the learning step to choose the smallest number of required parameters and thus to avoid the overfitting risk. The three approaches are compared in terms of accuracy and robustness. A cross-validation test is carried out on noisy data.