Characterization of neuronal cell growth provides key information about the development of neural pathways during embryogenesis as well as their regeneration following postnatal nerve injury. However, very active cell types like growing neurons are difficult to track in detail because their motion is complex and cellular features have a tendency to move out of the focal plane or occlude one another. Due to these difficulties, characterization of neuronal growth dynamics has been less quantitative than desired. To address this problem, we developed a method for the automated motion analysis of neuronal growth based on image analysis and shape correspondence techniques. This method increases the rate at which we can obtain dynamic data by at least an order of magnitude. We focus specifically on the growth cone, a specialized, cell-like structure at the growing neurite tip whose behavior is believed to be a key determinant of neuronal growth.