Oriented feature detectors are fundamental tools in image understanding, as many images display information of interest in the form of oriented features. Several oriented feature detectors have been developed; some of the important families of oriented feature detectors are steerable filters and Gabor filters. In this work, design criteria and performance analysis are presented for the following oriented feature detectors: the Gaussian second-derivative steerable filter; the quadrature-pair Gaussian second-derivative steerable filter; the real Gabor filter; the complex Gabor filter; and a line operator that has been shown to outperform the Gaussian second-derivative steerable filter in the detection of curvilinear structures in mammograms. The detectors are assessed in terms of their capability to detect the presence of oriented features as well as their accuracy in the estimation of the angle of the oriented features present in test images. It is shown that the real Gabor filter yields the best detection performance and angular accuracy, whereas the line operator and the steerable filter provide an advantage in terms of computational speed.