This paper presents an algorithm for automatic segmentation of small vehicle targets in MSTAR images. The segmenter is based on a histogram threshold technique and is able to detect both target vehicles and their shadows, and it is divided into three parts. First, the main component of the pre-processing part is a morphological closing filtering which decreases the intensity of speckle in images. The second part of the segmenter performs a histogram threshold operation. It is built around the use of the EFC-based model selection algorithm to estimate an image histogram with a mixture of normal densities, and a new method to compute thresholds. In this paper, we introduce a new linear method for computing multi-level thresholds from a mixture of normal densities. The post-processing operation is performed in order to remove any small detected artefacts other than targets of interest.