1. There are several reports that random dot patterns are potent stimuli for cortical complex cells but not for simple cells. This finding is regarded as evidence against Hubel and Wiesel's hierarchical model of cortical circuitry, in which simple cells are the principal input to complex cells. We have reinvestigated the question quantitatively by recording responses to dot patterns from 106 cells in area 17 and the 17/18 border region of normal adult cats. 2. The cells were classified as simple (n = 62) or complex (n = 40) (4 were end stopped or hypercomplex) on the basis of whether they gave modulated (AC) or unmodulated (DC) responses to drifting sine gratings. 3. Although there are large within-group differences, we found both simple and complex cells that respond to bright random dots on a dark background, drifted across the receptive field at 3 degrees/s. The responses at the optimal direction averaged 6.2 and 18.1 spikes/s (spontaneous activity subtracted) for simple and complex cells, respectively. 4. We also recorded responses to drifting sine gratings. Complex cells were also found to respond more than simple cells to these stimuli. For each cell, we calculated a dot index expressing the dot response relative to grating response. The dot index averaged 0.43 for simple cells and 0.55 for complex cells. It therefore appears that much of the difference in response to dot patterns reflects a difference in general responsivity. 5. In subsamples of cells, we examined the effects of varying dot density, dot size, and drift velocity. These variables affect different cells in a manner largely independent of cell class. Most simple cells in our sample responded well to random dot patterns at several velocities, at two different dot sizes and at both 3 and 50% dot densities. 6. Our results agree with previous studies in showing that complex cells respond more vigorously than simple cells to dot patterns, but the fact that many simple cells also respond to these stimuli makes our results consistent with a hierarchical model of cortical circuitry.