When a log is missing in a drilling hole, geologists hope to deduce it from others logs available in another part of the hole or in a neighbouring hole, in order to define the lithologic facies of the hole. This paper presents a neural network method to predict the missing log's measure from the other available log's measures. This method, based on Multi-Layer Perceptron (MLP) acts as a non linear regression method for the prediction task and as a probability density distribution approximation for the outlier rejection task. The result obtained when applied to actual log's data for prediction and rejection are presented in a separate section. The last section is dedicated to a non supervised neural method in order to reconstruct the lithologic facies of the concerned hole. This last experiment allows to validate and interpret the different results of the proposed methods.