The objective of this research was to develop a direct artificial neural network with the ability to predict a corrosion rate of metals in different biodiesel. Experimental values were obtained by the electrochemical noise technique, EN, as well as, information reported in the literature. A backpropagation model was proposed with three layers; metal and biodiesel composition, blend biodiesel/diesel, total acid number (TAN), temperature and exposure time were considered as input variables in the model. The best fitting training data were acquired with 24:4:1, considering a Levenberg –Marquardt learning algorithm, a hyperbolic tangent and linear transfer functions in the hidden and output layer respectively. Experimental and simulated data were compared satisfactorily through the linear regression model with a correlation coefficient of 0.9885 and a mean square error, MSE, of 2.15 × 10−4 in the validation stage. Furthermore, the model agreed the requirements of the slope and the intercept statistical test with a 99% confidence. The obtained results indicated that the ANN model could be attractive as corrosion rate estimator. •ANN model was applied to predict CR of metals in different biodiesel.•Database was collected by EN results and scientist available information.•Metal and environment composition, TAN, T and time were input variables.•ANN was successfully trained and validated by different database.•Unsaturated esters, metal ions and TAN greatly influenced the corrosion rate model.