In order to achieve wider acceptance among users of thematic maps derived from remote sensing data, the interpreter must be able to specify the accuracy of his product. This requires a valid sampling procedure to estimate classification accuracy. Although several alternative methods have been used in the past, none provide sufficient statistical justification for the allocation of sample points in each category of land use using remote sensing imagery. This paper describes a more detailed and more reliable method for determining the most appropriate (i.e., minimum,) sample size. The concept developed and described in the paper incorporates the probability of making incorrect interpretations at particular prescribed accuracy levels, for a certain number of errors, for a particular sample size. The remote sensing sampling strategy presented has the added advantage that it can easily be adapted for use with most forms of remote sensing imagery, including orbital data. It provides a reliable framework for testing the accuracy of any remote sensing image interpretation — based land use classification using the minimum number of sample points; thereby saving time and money, especially if it is employed in operational surveys where high specification accuracy levels need to be guaranteed.