High frequency flooding, sea level rise and changes to riverine sediment fluxes have threatened the habitable land area of river deltas, where close to half a billion people live, globally. Understanding shoreline positions is important for overall sustainable planning of deltaic communities and delta evolution predictive modeling. However, a gap in literature is recognized where there is a) no understanding of the most effective shoreline extraction method for a delta, and b) comparisons across techniques to infer on the performance metrics of techniques across deltas in different climate regions. This makes it difficult to apply existing knowledge to lesser studied, data sparse deltaic regions worldwide. In addressing these gaps, we evaluated the performance of 5 different remote sensing techniques against a hand-digitized shoreline vector of 44 river deltas globally, representing the 3 different morphological types of deltas (river-, tide- and wave-dominated), across 4 Köppen Climate Classes using Landsat 8 imagery. We propose a new metric (Robustness: R) to evaluate the performance of a given technique. The results show that 1) the best performing method for the majority of the deltas (35/44) was Unsupervised Classification, 2) there is no geographical significance in the performance of the tested techniques, and 3) wave dominated deltas showed the highest classification robustness while tide dominated deltas showed the lowest. Recommendations are made for the application of techniques in different types of deltas and unknown deltaic territories worldwide.