In this paper, we describe the algorithm that will be used during the upcoming Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission for discriminating between clouds and aerosols detected in two-wavelength backscatter lidar profiles. We first analyze single-test and multiple-test classification approaches based on one-dimensional (1-D) and multidimensional probability distribution functions (PDFs) in the context of a two-class feature identification scheme. From these studies we derive an operational algorithm. This algorithm is a 3-D approach utilizing the layer mean attenuated backscatter at 532 nm, the layer-integrated 1064-nm to 532-nm volume color ratio, and the midlayer altitude. A data set acquired by the Cloud Physics Lidar (CPL) is used to test the algorithm. Comparisons are conducted between the 3-D CALIPSO algorithm results and those derived from an existing 2-D algorithm. The results obtained show generally good agreement between the two methods. However, of a total of 228,264 layers analyzed, ~5.7% are classified as different types by the two algorithms. This disparity is shown to be due largely to the misclassification of optically thin clouds as aerosols by the 2-D algorithm. The use of 3-D PDFs in the CALIPSO algorithm is found to significantly reduce this type of error because the separation between cloud and aerosol clusters is more complete in this 3-D space. Dust presents a special case. Because the intrinsic scattering properties of dust layers can be very similar to those of clouds, additional algorithm testing was performed using an optically dense layer of Saharan dust measured during the Lidar In-space Technology Experiment (LITE). In general, the method is shown to distinguish reliably between dust layers and clouds. The relatively few erroneous classifications that occurred most often in the analysis of the LITE data occurred, in those regions of the Saharan dust layer where the optical thickness was the highest.