Abstract Although automated object surface reconstruction via feature-based matching is commonly employed in both stereoscopic close range and topographic photogrammetry, it has rarely been used in conjunction with multi-image, convergent photogrammetric networks. A prerequisite for feature-based matching of distinct points is the application of interest operators to provide the dense arrays of candidate feature points within the images forming the network. This paper describes an evaluation of three interest operators, the Förstner, SUSAN and FAST algorithms, the aim being to assess which is optimal for feature-based matching in convergent close range photogrammetry. Following a brief review of the development of interest operators, a description of the three operators is provided, with the recently developed FAST operator being discussed in more detail. The merits of image pre-processing via the Wallis filter are also outlined, after which the performance of the interest operators is experimentally assessed within an eight-image network on the basis of accuracy of interest-point localisation, detection rate and speed. The findings of the evaluation are that, of the Förstner, SUSAN and FAST operators, the FAST, which has not been employed to a significant extent in photogrammetry to date, is optimal for feature-based matching in multi-image close range networks.