Wireless technology has permitted wireless monitoring and control applications for industrial process plants. Most related works have addressed delay issues in networked control system using common predictive techniques such as Smith predictor, and Kalman filter. However, they are complex and only applicable for high performance controllers. Thus, they are not suitable for implementation at microcontroller-based plant actuators which are normally computationally limited. A suitable approach to overcome this shortcoming is to use exponentially weighted moving average (EWMA) filter which is computationally efficient while still achieving desired control performance. Common practice for design of EWMA filters is to fix the filter weight. The drawback of this approach is that it results in longer time to reach the desired setpoint. Therefore, in this paper, a dynamically tuned EWMA filter is proposed to improve control performance of the process plant. Here, the weight of the filter is tuned adaptively using fuzzy logic technique. From the results obtained, it is shown that the proposed method is robust against time-varying delay and significantly improves the control performance by reducing both settling time and percent overshoot. © 2020 ACM.