The stock status in many small scale fisheries is often unknown, usually because of limited or absence of data, particularly in the global south. Many data-limited assessments are oriented toward an assessment of fishing pressure. Assessments are often limited by the processes involved in quantifying the uncertainties associated with process and observation errors. Using catch and length data, we evaluated and compared the performance of stock assessment models for Ethmalosa fimbriata in The Gambia. As catch-based models, we used an extension of the Maximum Sustainable Yield "MSY" catch (CMSY++) and a Bayesian length-based model (LBB). Both LBB and CMSY++ models provided good results. Indeed, this case study provides guidelines to consider when using length- and catch-based pseudo-cohort models for data-poor fisheries. Consequently, the indicators estimated for E. fimbriata in The Gambia can be used for sustainable management. Both models showed that the E. fimbriata stock was in a state of recovery with reduced fishing pressure. Reducing fishing efforts could, therefore, help improve stock management. Finally, we advocate further investigation of simple empirical management procedures, irrespective of data limitations, because of their ability to achieve fisheries management objectives when the data context requires such an approach.