A new algorithm called the flying window locator algorithm (FWLA) is presented for locating multiple targets in complex images, in which the illumination is not uniform, the sizes of targets are small, and the colors of targets and their background are similar. The FWLA divides a complex image into subimages, called windows. To guarantee that a target of interest is completely in one of the windows, neighboring windows are overlapped so that each overlapped area is bigger than that of a target. Since a window defined in the FWLA is small, a scene component in a window is considered as having a similar shading or color. Hence, a window is composed of a mixture of component distributions, stemming from the different background and target regions in the window. A gradient clustering algorithm (GCA) is developed to separate a mixture in each window and segment a window. After segmentation, a `hole' concept is used to find the targets of interest based upon a priori knowledge of the target size, shape, and color. The FWLA is a non-iterative clustering algorithm and uses only fixed- point numbers to analyze an image. Consequently, it is fast and computationally efficient. Results of applying the FWLA to two different computer vision problems are presented.