Unlike the horizontal axis wind turbines, only a few studies have been conducted recently to improve the performance of a Darrieus Vertical Axis Wind Turbine with straight blades (H-type VAWT). Pitch angle control technique is used to enhance the performance of an H-type VAWT in terms of power output and self-starting capability. This thesis aims to investigate the performance of an H-type VAWT using an intelligent blade pitch control system. Computational Fluid Dynamics (CFD) is used to determine the optimum pitch angles and study their effects on the aerodynamic performance of a 2D H-type VAWT at different Tip Speed Ratios (TSRs) by calculating the power coefficient (Cp). The results obtained from the CFD model are used to construct the aerodynamic model of an H-type VAWT rotor, which is required to design an intelligent pitch angle controller based on Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN) method. The performance of the blade pitch controller is investigated by adding a conventional controller (PID) to the MLP-ANN controller (i.e., Hybrid controller). For stability analysis, an H-type VAWT is modeled in nonlinear state space by determining the mathematical models for an H-type VAWT components along with Hybrid control scheme. The effectiveness of proposed pitch control system and the CFD results are validated by building an H-type VAWT prototype. This prototype is tested outdoor extensively at different wind conditions for both fixed and variable pitch angle configurations. Results demonstrate that the blade pitching technique enhanced the performance of an H-type VAWT in terms of power output by around 22%.