This paper discusses an approach to incorporate Artificial Neural Network (ANN) based kinetics modeling into Large-Eddy Simulation (LES) of reactive flows. The emphasis has been spent for replacing stiff ordinary differential equation (ODE) solvers with ANN for chemical kinetics calculations. A back-propagation type of an ANN code has been developed and its performance has been tested in laminar and turbulent methane/air and syngas/air premixed combustion processes. The ANN is trained using an independent premixed flame calculation and then used as is in the LES. Results indicate that the accuracy of ANN predictions for turbulent flow computations highly depend on the initial training data-set. If well trained, ANN can succesfully predict the chemical state-space using less memory than a conventional look-up table approach and in a computationally efficient manner than the stiff ODE solvers.