To achieve robust and efficient object recognition, particularly from real outdoor images, we must develop methods to reduce clutter and extract salient information of objects. Toward this end, we present a technique to rank and extract salient contours from a 2-D image acquired by a passive sensor. The goal is to find important contours corresponding to possible objects. Our method starts with edge pixels, or edgels, from an edge detector and assigns a saliency measure to linked edgels (contours) based on length, smoothness, and contrast. For length we use the number of edgels in the contour, for smoothness we use average change of curvature, and for contrast we use the edge magnitude. Contours are ranked by saliency, and the more salient contours are selected. We test this method on several real outdoor images of objects in cluttered and occluded conditions and obtain excellent results. We evaluate the performance of this technique in the context of a recognition system that matches 2-D image corners with 3-D model vertices. We present graphs, using corners on the object of interest and clutter, to demonstrate the appropriateness of saliency ranking. We plot curves displaying the percentage of object corners to all image corners for the top few salient contours. We conclude by observing that extracting the more salient contours increases the ratio of image corners on the object to all image corners, reducing the search space for the corner matching step in recognition.