The cerebral cortex is distinguished by layers of neurons of different morphologies and densities. The layers are formed by the migration of newly generated neurons from the ventricular zone to the cortical plate near the outer (pial) boundary of the cortex, along radial paths approximately perpendicular to the cortical surface. Immunochemical labelling makes these cells' patterns visible in brightfield microscopy so that layer formation can be studied. We developed a suite of programs that automatically digitize the entire cortex, identify the labelled cells and compute cell densities along local radial paths. Cell identification used supervised classification on all the significantly stained objects corresponding to maxima in lowpass filtered versions of the digital microgrphs. Classification of all the stained objects as cells or noncell objects was made by a decision rule based on morphometric and grey-level texture features, including features based on Gabor functions. Detection sensitivity and classification accuracy were jointly maximized on training data consisting of about 3000 expert-identified neurons in micrographs. Total program performance was tested on a separate (test) set of labelled neurons the same size as the training data set. The program detected 85% of the cells in the test set with a total error of 0.19. The identified cells' locations were used to compute population densities along normals to the cortical layers, and these densities served as a measure of neuronal migration. Transcortical density profiles obtained by computation and by manual cell counting were very similar. The cell identification program was built on well-established methods in statistical pattern recognition and image analysis and should generalize readily to other histological preparations.