Quantitative brain tissue segmentation from newborn MRI offers the possibility of improved clinical decision making and diagnosis, new insight into the mechanisms of disease, and new methods for the evaluation of treatment protocols for preterm newborns. Such segmentation is challenging, however, due to the imaging characteristics of the developing brain. Existing techniques for newborn segmentation either achieve automation by ignoring critical distinctions between different tissue types or require extensive expert interaction. Because manual interaction is time consuming and introduces both bias and variability, we have developed a novel automatic segmentation algorithm for brain MRI of newborn infants. The key algorithmic contribution of this work is a new approach for automatically learning patient-specific class-conditional probability density functions. The algorithm achieves performance comparable to expert segmentations while automatically identifying cortical gray matter, subcortical gray matter, cerebrospinal fluid, myelinated white matter and unmyelinated white matter. We compared the performance of our algorithm with a previously published semi-automated algorithm and with expert-drawn images. Our algorithm achieved an accuracy comparable with methods that require undesirable manual interaction.