Many medical image processing applications are based on the detection of corresponding features. In digital images, features are depicted by one or more digital points. Hence, feature correspondence is achieved through the estimation of point correspondences between the compared images. This paper presents and evaluates five of the most common and recent techniques for estimating corresponding points between two flat digital images. The featured techniques include Template Matching, the Iterative Closest Points algorithm, Correspondence by Sensitivity to Movement, the Self-Organizing Maps and the Artificial Immune Network algorithm. All methods are presented, mainly focusing on their distinct characteristics. The featured techniques were tested both qualitatively and quantitatively on an extensive set of medical image pairs, including images subject to both known and unknown initial geometrical deviations. Each of the five methods was evaluated on all 263 available image pairs in terms of correspondence and registration accuracy. After assessing the point correspondence accuracy of each method, it was deduced that their performance depends on the characteristics of the featured data set. However, the Artificial Immune Network approach outperformed in most cases the rest of the featured point-correspondence methods, closely followed by the Self Organizing Maps algorithm.