In this work, a computer-aided tool is developed to predict relevant physical and mechanical properties that are involved in the selection tasks of metallic materials. The system is based on the use of artificial neural networks supported by big data collection of information about the technological characteristics of thousands of materials. Thus, the volume of data exceeds 43k. The system can access an open online material library (a website where material data are recorded), download the required information, read it, filter it, organise it and move on to the step based on artificial intelligence. An artificial neural network (ANN) is built with thousands of perceptrons, whose topology and connections have been optimised to accelerate the training and predictive capacity of the ANN. After the corresponding training, the system is able to make predictions about the material density and Young's modulus with average confidences greater than 99% and 98%, respectively. INDEX TERMS Artificial intelligence, big data, material selection, multilayer feedforward networks, neural network, property prediction, software-based web browser control. LIST OF SYMBOLS AND ABBREVIATIONS ADAM Adaptive Moment Estimation AI Artificial intelligence ANN Artificial Neural Networks β n ADAM algorithm parameter ADAM stability factor ε Prediction error of a neural network η ADAM step size f Error function g Gradient of the error function HTML HyperText Markup Language m ADAM first moment estimate v ADAM second moment estimate w Weights vector