The understanding of any neural circuit requires the identification and characterization of all its components. Morphologic classifications of neurons are, therefore, of central importance to neuroscience. We use a quantitative method to classify neurons from layer 5 of mouse primary visual cortex, based on multidimensional clustering. To reconstruct neurons, we used Golgi impregnations and biocytin injections, as well as DiOlistics, a novel technique of labeling neurons with lipophilic dyes. We performed computerized 3-D reconstructions of 158 layer 5 cells to measure a series of morphologic variables. Principal component analysis and cluster analysis were used for the classification of cell types. Five major classes of cells were found: group 1 includes large pyramidal neurons with apical dendrites that reach layer 1 with an apical tuft; group 2 consists of short pyramidal neurons and large multipolar cells with "polarized" dendritic trees; group 3 is composed of less extensive pyramidal neurons; group 4 includes small cells; and group 5 includes another set of short pyramidal neurons in addition to "atypically oriented" cells. Our sample included a relatively homogeneous group of 27 neurons that project to the superior colliculus, which clustered mainly in group 1, thus supporting the validity of the classification. Cluster analysis of neuronal morphologies provides an objective method to quantitatively define different neuronal phenotypes and may serve as a basis for describing neocortical circuits.