Detection and identification of objects in images formed by coherent imaging systems are complicated by the presence of speckle. Speckle not only complicates these problems for human observers, but also for machine detection and identification algorithms. We investigate optimal statistical tests for object discrimination and orientation determination in speckle and compare their performance to that of human observers for the same problems. We formulate maximum likelihood tests for determining the orientation of an object and for discriminating among a set of known objects in a speckled image. We then analyze the performance of these tests to study the system requirements for reliable object discrimination and orientation determination. Next we generalize these tests and their corresponding pertormance analyses into three broad classes of pattern recognition problems, corresponding to orthogonal, antipodal, and biorthogonal signal problems in statistical communications theory. These generalizations make the design and analysis of a broad range of object discrimination and orientation determination straightforward. Finally we compare the performance of these tests to the results of Korwar and Pierce for human interpretation of objects in speckled images. We note that for fixed image contrast, number of looks, and image size in pixels, object shape has no effect on machine detection performance. This is not true for the human observer.