In this paper, a recursive strategy is applied to identify gross errors (biases and leaks) and estimate their magnitude in linear steady state processes. A recursive search scheme is used first to isolate the candidate sources of gross errors and then simultaneous identification/estimation of gross errors is accomplished. A recently proposed equivalency theory is used to correctly assess results. Comparative studies of performance and accuracy of estimation are performed for some process networks when compared to existing techniques. Simulation results show that the proposed approach has higher performance to identify gross errors as well as to estimate their magnitudes.