Motivation: Automatic recognition of cell identities is critical for quantitative measurement, targeting and manipulation of cells of model animals at single-cell resolution. It has been shown to be a powerful tool for studying gene expression and regulation, cell lineages and cell fates. Existing methods first segment cells, before applying a recognition algorithm in the second step. As a result, the segmentation errors in the first step directly affect and complicate the subsequent cell recognition step. Moreover, in new experimental settings, some of the image features that have been previously relied upon to recognize cells may not be easy to reproduce, due to limitations on the number of color channels available for fluorescent imaging or to the cost of building transgenic animals. An approach that is more accurate and relies on only a single signal channel is clearly desirable.Results: We have developed a new method, called simultaneous recognition and segmentation (SRS) of cells, and applied it to 3D image stacks of the model organism Caenorhabditis elegans. Given a 3D image stack of the animal and a 3D atlas of target cells, SRS is effectively an atlas-guided voxel classification process: cell recognition is realized by smoothly deforming the atlas to best fit the image, where the segmentation is obtained naturally via classification of all image voxels. The method achieved a 97.7% overall recognition accuracy in recognizing a key class of marker cells, the body wall muscle (BWM) cells, on a dataset of 175 C. elegans image stacks containing 14 118 manually curated BWM cells providing the 'ground-truth' for accuracy. This result was achieved without any additional fiducial image features. SRS also automatically identified 14 of the image stacks as involving ±90° rotations. With these stacks excluded from the dataset, the recognition accuracy rose to 99.1%. We also show SRS is generally applicable to other cell types, e.g. intestinal cells.