The presence of multiple modes in a histogram gives important information about data distribution for a great amount of different applications. The dip test has been the most common statistical measure used for this purpose. Histograms of oriented gradients (HOGs) with a high bimodality have shown to be very useful to detect highly robust keypoints. However, the dip test presents serious disadvantages when dealing with such histograms. In this paper we describe the drawbacks of the dip test for determining HOGs bimodality, and present a new bimodality test, based on mathematical morphology, that overcomes them. © 2012 Springer-Verlag.