Gross error detection (GED) is an important function in automated processing of plant data. All GED tests developed so far are based on a linear theory and can be applied to nonlinear processes only after suitable linearization of the process constraints. In this paper, we propose a test for GED in nonlinear processes which does not require the constraints to be linearized. Although the proposed test does not have a rigorous statistical basis, it is entirely analogous to the generalized maximum Likelihood ratio test. This test is combined with different existing strategies for multiple GED to determine the best possible method. Simulation results show that for a significantly nonlinear system the proposed test performs better than tests which rely on linearizing the constraints. However, for mildly nonlinear systems such as those with only bilinear constraints, the performances are comparable. The simple serial compensation strategy is shown to be better than its modified version as well as the serial elimination strategy, especially when the aim is to maximize accuracy of the final estimates.