Object shape in an image can vary for many reasons. Therefore, a goal of object recognition research is to create algorithms that are able to accurately recognize objects with variability in shape. This paper suggests recognizing shape through an assessment of different measures. There are two primary approaches to measuring shape variations for recognition: measure, compare, and match; and compare, measure, and match. In the first approach, attributes of the object shape are measured and compared with the same measured attributes of the template shape. This paper focuses on the second approach, which first compares the object and template jointly and then creates a normalized measure for matching. This approach is called multiple joint comparative normalized measures (MJCNM). Confidence in the match is shown to be better against certain shape variabilities on using MJCNM than on using just one shape measure. In particular, the MJCNM approach here uses matched-filter, Procrustes, partial-directed Hausdorff, and percent-pixels-same measures. An experimental result is given that demonstrates the implementation and usefulness of that approach.