Abstract: Active Noise Control (ANC) involves an electro acoustic or electromechanical system that cancels the primary (unwanted) noise based on the principle of superposition. An anti-noise signal of equal amplitude and opposite phase is generated and combined with the primary noise, resulting in the cancellation of the noise. A fundamental problem to be considered in ANC systems is the requirement of highly precise control, temporal stability and reliability. To produce high degree of attenuation, the amplitude and phase of both the primary and the secondary noise must match with the close precision. The adaptive filters are used to control the noise and it has a linear input and output characteristic. If a transfer path of the noise has nonlinear characteristics it will be difficult for the filter to generate an optimal anti-noise. In this study, we propose a algorithm, delta rule algorithm which uses non linear output function. Delta rule is used for learning complex patterns in Artificial Neural Networks. We have implemented the adaptive filters using Least Mean Square (LMS) algorithm, Recursive Least Square (RLS) algorithm and Delta rule algorithm and compared the results.