In this work, preliminary quantitative results are presented that characterize the cortical surface during neurosurgery using a laser range scanner. Intra-operative cortical surface data is collected from patients undergoing cortical resection procedures and is registered to patient-specific pre-operative data. After the skull bone-flap has been removed and the dura retracted, a laser range scanner (LRS) is used to capture range data of the brain's surface. An RGB bitmap is also captured at the time of scanning, which permits texturing of the range data. The textured range data is then registered to textured surfaces of the brain generated from pre-operative images. Registration is provided by a rigid-body transform that is based on iterative-closest point transforms and mutual information. Preliminary results using the LRS during surgery demonstrate a good visual alignment between intra-operative and pre-operative data. The registration algorithm is able to register surfaces using both sulcal and vessel patterns. Target registration errors on the order of 2mm have been achieved using the registration algorithm in a clinical setting. Results from the analysis of laser range scan data suggest that the unique feature-rich cortical surface may provide a robust method for intra-operative registration and deformation measurement. Using laser range scan data as a non-contact method of acquiring spatially relevant data in a clinical setting is a novel application of this technology. Furthermore, the work presented demonstrates a viable framework for current IGS systems to computationally account for brain shift.