Brushless Direct Current (BLDC) motors are widely used in industrial applications and PID controllers are usually applied to achieve the performance characteristics of BLDC motors. There are different methods for setting up PID parameters that one of them has been called Internal Model Control (IMC) which is used as  parameter to modify the performance characteristics of a system. Sometimes setting up IMC PID parameters are hard so in this thesis for controlling of performing purpose, so ANN is used to add proper values with IMC PID coefficients. The proposed controller used an ANN-based coefficient modifier because it can train easily and help the system to achieve desired robustness and performance characteristics. The proposed system used the Neural Network system that is extracted from the training ANN system with desired data to improve BLDC motor performance. In this thesis, the controller way is to propose the strategy and simulate the results of a control system. The response of the system has been studied for various speed and load conditions using MATLAB simulation tools. The effectiveness of the proposed control scheme is verified by developing the simulation results for the BLDC motor as a study in MATLAB/SIMULINK software. It is observed from the simulation results that the proposed neural network-based PID of the BLDC motor performance specification has 0.006025sec rise time, 0.006074 settling time for 0.1 rad disturbance for starting phase angle, and 0.98% maximum overshoot, and also 0.0035 peak time. So to improve the performance and robustness of the motor.