Artificial neural network (ANN) is applied to investigate the hydrodesulfurization (HDS) process with light-cycle oil as feed and NiMo/Al2O3 as catalyst. ANN models frequently work as a "black box" which makes the model invisible to users and always need significant data for training. In this work, a new ANN is proposed. The Langmuir-Hinshelwood kinetic mechanism is incorporated into the model so that the proposed ANN model is forced to follow the given reaction mechanisms. Both advantages of self-learning ability of ANN and the existing knowledge of HDS were taken into account. Lengthy training process is minimised. Effects of operating temperature, pressure, and LHSV on the sulfur removal rate are studied. The inhibition of nitrogen compounds is also investigated. It is shown that the presence of nitrogen can significantly reduce the conversion rate of sulfur components, in particularly, hard sulfur such as 4,6-DMDBT.