The Hough transform is known to be an effective technique for target detection and track initiation in search radars. However, most papers have focused on the simplistic applications of this technique which consider a 2-D data space for the Hough transform. In this paper, a new method based on xthe Hough transform is introduced for detecting targets in a 3-D data space. The data space is constructed from returned surveillance radar signal using the range and bearing information of several successive scans. This information is mapped into a 3-D x-y-t Cartesian data space. Targets are modeled with four parameters in this data space. The proposed 3-D Hough detector is then used to detect the existent targets in the 3-D surveillance space by mapping the returned signal of the radar from the data space to the parameter space. This detector, which is constructed of two detection stages, integrates the returned data of each target non-coherently along its 3-D trajectory in one parameter space cell related to this target. Hence, the detection performance will improve. The effectiveness of the new 3-D Hough detector is demonstrated through deriving the detection statistics analytically and comparing the results with those of several comprehensive simulations. The performance improvement of this detector is shown by comparing its detection range with the conventional detector. The proposed detector is also evaluated with real radar data and its efficiency is confirmed.