Optimum bit selection is one of the important issues in drilling engineering. Usually, optimum bit selection is determined by the lowest cost per foot and is a function of bit cost and performance as well as penetration rate. Conventional optimum rock bit selection program involves development of computer programs created from mathematical models along with information from previously drilled wells in the same area. Based on the data gathered on a daily basis for each well drilled, the optimum drilling program may be modified and remised as unexpected problems arose. The approach in this study uses the power of Artificial Neural Networks (ANN) and fractal geostatistics to solve the optimum bit selection problem. In order to achieve this goal a back-propagation ANN model was developed by training the model using real rock bit data for several wells in a carbonate field. The training and fine-tuning of the basic model involved use of both gamma ray and sonic log data. After that the model was tested using various drilling scenarios in different lithologic units. It as observed that the model provided satisfactory results. (C) 2002 Elsevier Science Ltd. All rights reserved.