Unlike horizontal axis wind turbines (HAWTs), the Darrieus vertical axis wind turbine (H-type VAWT) has been the subject of only a few recent studies directed at improving its self-starting capability and/or aerodynamic performance. The technique currently used for improving the performance of this type of turbine is pitch angle control. This paper presents intelligent blade pitch control for enhancing the performance of H-type VAWTs with respect to power output. To determine the optimum pitch angles, ANSYS Fluent Computational Fluid Dynamics (CFD) software was used for a study of the aerodynamic performance of a 2D variable pitch angle H-type VAWT at a variety of tip speed ratios (TSRs). For each case examined, the power coefficient (C-p) was calculated and compared to published experimental and CFD findings. The results obtained from the CFD model were then applied for the construction of an aerodynamic model of an H-type VAWT rotor, which constituted a prerequisite for designing an intelligent pitch angle controller using a multilayer perceptron artificial neural network (MLP-ANN) method. The performance of the MLP-ANN blade pitch controller was compared to that of a conventional controller (PID). The findings demonstrate that for an H-type VAWT, compared to a conventional PID controller, an MLP-ANN results in superior power output. (C) 2017 Elsevier Ltd. All rights reserved.