In this study, the application of artificial neural network (ANN) method in predicting the density of alkali metals and their mixtures is investigated. A total number of 595 different data points of these compounds were used to train, validate and test the model. A typical three-layer feedforward backpropagation neural network has been trained by the Levenberg Marquardt algorithm. The tansig-tansig transfer functions with 15 neurons in the hidden layer makes the least error, so a network with (8-15-1) structure was used to design the ANN model. The average relative deviations for train, validation, and test sets are 0.1029, 0.1396, and 0.1002, respectively. A comparison between our results and those obtained from some previous works shows that this work, as an excellent alternative, can provide a simple procedure to predict the density of these compounds in a better accord with experimental data up to high temperature, high pressure (HTHP) conditions. (C) 2013 Elsevier B.V. All rights reserved.