For a linear system having event based measurements and correlated noises, a state estimation algorithm is proposed. A general event based sampling is employed to obtain the measurements, where in the case of unavailability of measurements, event based strategy itself is used to obtain approximate state and covariance estimates. To deal with correlated noises, a two-step ahead prediction approach is employed to obtain recursive equations for estimated state and covariance. The obtained results are illustrated using a simulation example. © 2019