In many image processing applications, the gray levels of pixels belonging to the object are quite different from the levels belonging to the background. Thresholding becomes then a simple but effective tool to separate objects from the background. This segmentation tool is being used in many research and operational applications, so attempts to automate thresholding has been a permanent area of interest. However, several difficulties impede to achieve in all the situations the desired results, so for any specific problem, the different techniques will have to be tested in order to select those providing the best performance. In this paper we have conducted a survey of image thresholding methods with a view to assess their performance when applied to remote sensing images and especially in oceanographic applications. Those algorithms have been categorized into two groups, local and global thresholding techniques, and the global ones again classified according to the information they are exploiting. This classification has lead to histogram shape-based methods, clustering-based methods, entropy-based methods, object attribute-based methods and spatial methods. After the application of a total of 36 techniques to visible, IR and microwave (synthetic aperture radar) remote sensing images, the optimum methods for each one have been selected.