We present a system for shape tolerant three-dimensional (3D) recognition of biological microorganisms using holographic microscopy. The system recognizes 3D microorganisms by analyzing complex images of the 3D microorganism restored from single-exposure on-line (SEOL) digital hologram. In this technique the SEOL hologram is recorded by a Mach-Zehnder interferometer, and then the original complex images are reconstructed numerically at different depths by inverse Fresnel transformation. For recognition, a number of sampling segment features are arbitrarily extracted from the restored 3D image. These samples are processed using a number of cost functions and the sampling distributions for the difference of the parameters (location, dispersion) between the sample segment features of the reference and input 3D image are calculated using a statistical sampling method. Then, a hypothesis testing for the equality of the parameters between reference and input 3D image is performed for a statistical decision about populations on the basis of sampling distribution information. Student's t distribution and Fisher's F distribution are used to statistically analyze the difference of means and the ratio of variances of two populations, respectively. The proposed system is designed to be tolerant to recognizing various, plain microorganisms with analogous shape such as bacteria and algae. Preliminary experimental results are presented to illustrate the robustness of the proposed recognition system using statistical inference.