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The use of the artificial neural networks in management is common. Employing artificial intelligence allows businesses to gain a commercial advantage. The objective of the present paper is to apply an artificial neural network as a modeling and simulation technique, to determine the importance of influences of four input data on the output data in human resources evaluation. The data are organized in 14,999 datasets: satisfaction level of the employee, the average monthly hours worked, the project number that the employee participated in, and time spent by company for employee care. The used neural network is of feed forward type, with three layers (1 input, 1 hidden, 1 output) and with 9 (13) hidden neurons. Several functions for activation and solver were applied for finding the most accurate one. The simulations concluded that the most important influence is that of the average monthly hours followed closely by the employee level of satisfaction.