The Modified Iterative Measurement Test (MIMT) gross error detection algorithm has been improved using nonlinear programming techniques to improve its robustness and performance. Both data reconciliation and estimation of gross errors in MIMT can utilize nonlinear programming (NLP) techniques. The algorithm has been tested on a CSTR example and shows improved robustness compared to existing gross error detection algorithms. Therefore this enhanced algorithm appears to be quite promising for data reconciliation and gross error detection of highly nonlinear processes in chemical engineering.