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We introduce optical imaging techniques for three-dimensional (3-D)visualization and identification of microorganisms. Three-dimensional sensing and reconstruction is performed by single-exposure on-line (SEOL)digital holography. A coherent microscope-based Mach-Zehnder interferometer records Fresnel digital holograms of microorganisms. Complex amplitude holographic images are computationally reconstructed at different depths by an inverse Fresnel transformation. For pattern recognition/identification, two approaches are addressed. One is 3-D morphology-based recognition and the other is shape-tolerant 3-D recognition. In the first approach, a series of image recognition techniques is used to analyze 3-D geometrical shapes of microorganisms, which is composed of magnitude and phase distributions. Segmentation, feature extraction, graph matching, feature selection, training, and decision rules are presented. For the second approach, a number of sampling segments are arbitrarily extracted from the reconstructed 3-D biological microorganism. These samples are processed using a number of cost functions and statistical inference theory for the equality of means and equality of variances between the sampling segments. Experimental results with sphacelaria alga, tribonema aequale alga, and polysiphonia alga are presented.