Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit. 152157Siqueira, C., Antes tarde do que sem contratos. (In Portuguese) (2005) Brasil Energia, 298, pp. 26-28Unneland, T., Hauser, M., Real-Time Asset Management: From Vision to Engagement-An Operator's Experience (2005) Proc. SPE Annual Technical Conference and Exhibition, , Dallas, USAIversen, F.P., Cayeux, E., Dvergsnes, E.W., Gravdal, J.E., Vefring, E.H., Mykletun, B., Torsvoll, A., Merlo, A., Monitoring and Control of Drilling Utilizing Continuously Updated Process Models (2005) Proc. IADC/SPE Drilling Conference, , Miami, USAThonhauser, G., Wallnoefer, G., Mathis, W., Ettl, J., Use of Real-Time Rig-Sensor Data To Improve Daily Drilling Reporting, Benchmarking, and Planning - A Case Study (2006) Proc. Intelligent Energy Conference and Exhibition, , Amsterdam, The NetherlandsBourgoyne Jr., A.T., Young Jr., F.S., A multiple regression approach to optimal drilling and abnormal detection (1974), pp. 371-384. , 4R.V. Barragan, Otimização dos parâmetros mecânicos nas brocas para obter o custo mínimo de uma fase de um poço, (In Portuguese) Masters Dissertation, Faculdade de Engenharia Mecânica, Universidade Estadual de Campinas. Campinas, Brazil, 1995Mohaghegh, S., Arefi, R., Ameri, S., Aminiand, K., Nutter, R., Petroleum reservoir characterization with the aid of artificial neural networks (1996) Journal of Petroleum Sciences and Engineering, 16 (4), pp. 263-274Wang, F., Wang, X.J., Ma, Z.Y., Yan, J.H., Chi, Y., Wei, C.Y., Ni, M.J., Cen, K.F., The research on the estimation for the NOx emissive concentration of the pulverized coal boiler by the flame image processing technique (2002) Fuel, 81 (16), pp. 2113-2120Thaler, M., Grabec, I., Poredos, A., Prediction of energy consumption and risk of excess demand in a distribution system (2005) Physica A: Statistical Mechanics and its Applications, 355 (1), pp. 46-53Chong, A.Z.S., Wilcox, S.J., Ward, J., Prediction of gaseous emissions from a chain grate stoker boiler using neural networks of ARX structure (2001) IEE Proceedings Science, Measurement & Technology, 148 (3), pp. 95-102Coelho, D.K., Roisenberg, M., Freitas Filho, P.J., Jacinto, C.M.C., Risk Assessment of Drilling and Completion Operations in Petroleum Wells Using a Monte Carlo and a Neural Network Approach (2005) Proc. Winter Simulation Conference, , Orlando, USADashevskiy, D., Dubinsky, V., Macpherson, J.D., Application of neural networks for predictive control in drilling dynamics (1999) Proc. SPE Annual Technical Conference and Exhibition, , Houston, USABilgesu, H.I., Tetrick, L.T., Altmis, U., Mohaghegh, S., Ameri, S., A new approah for the prediction of rate of penetration (ROP) values (1997) Proc. SPE Eastern Regional Meeting, , Lexington, USANorgaard, M., Ravn, O., Poulsen, N.K., Hansen, L.K., (2000) Neural networks for modeling and control of dynamic systems: A practitioner's handbook, , London, UK: SpringerHaykin, S., (1999) Neural networks: A comprehensive foundation, , Upper Saddle River, USA: Prentice-HallMarquardt, D., An algorithm for least-squares estimations of nonlinear parameters (1963) SIAM Journal on Applied Mathematics, 11 (2), pp. 431-441Siegel, A.F., (1988) Statistics and Data Analysis, , New York, USA: John Wiley & Sons