In this work, a minimum variance estimator is designed for a networked system with inherent network imperfections in both sensor to estimator (S-E) and controller to actuator (C-A) channels simultaneously. The channels are affected by packet delays, dropouts, and uncertain observations. These effects are modeled using five Bernoulli distributed random variables. Correlation of noise at neighboring time caused by random delay is avoided by introducing two additional variables in the augmented stochastic model. The developed augmented stochastic model can handle network imperfections in both the S-E and C-A channels simultaneously. A minimum variance recursive linear estimator is designed using an innovation approach and projection theorem. Furthermore, sufficient condition is presented for the existence of steady state property of the proposed estimator. Simulation studies are carried out for the proposed estimator using a numerical example and a single link robot arm. Finally, performance comparison with other popular filters shows the effectiveness of the designed estimator. © 2020 John Wiley & Sons, Ltd.