Geographic object-based image analysis (GEOBIA) is a promising methodology for image analysis, in which images are first segmented into image segments (or objects) and then analysed based on shape, texture, context and spectral features. The extra dimension of data offered by the objects yields a more enhanced image analysis. The first and most important step is thus the segmentation of images. The effectiveness of the object-based image analysis depends entirely on the quality of the segmentation result. There exist several types of image segmentation algorithms developed for a variety of applications ranging from medical imaging to remote sensing image analysis. It is, therefore, necessary to have an evaluation measure to decide which algorithm can be better for a particular task. Like segmentation itself, there is no standard way of evaluating segmentation results. In this article, we provide an easy way to analyse segmentation results by defining what qualifies as under-segmentation and over-segmentation while analysing the segmentation results of user-selected reference regions. The evaluation criteria are designed to handle the results of multi-level segmentation algorithms, which are commonly used in GEOBIA.