Statistical techniques for the analysis of cellular alignment data are currently limited due to heuristic and qualitative approaches. For example, generally a cut-off degree limit, commonly 20 degrees, is arbitrarily defined within which cells are considered ‘aligned.’ The efficacy of patterned biomaterials in guiding the alignment of cells, such as neurons, is often critical in correlating materials design with biological outcomes. We developed a statistical algorithm for processing cellular alignment data, which implicitly determines an ‘angle of alignment.’ This was accomplished in the following manner: 1) Neuronally-differentiated PC-12 cells were seeded on flat and micropatterned silk fibroin films. Cells ‘aligning’ with an underlying, anisotropic scaffold display uniformly distributed angles up to a cut-off point, determined by how effective the biomaterial is in aligning cells. 2) We then determined where the data sets diverged from the uniform distribution by measuring the spacing between the collected, increasingly ordered angles, modeling this spacing as a time series. 3) This time series was then analyzed for uniformity using a normalized cumulative periodogram (NCP)-criterion. Following this protocol, we were able to define cellular alignments for the patterned silk films, of dimensions 300 patterns/mm, with 8° and 17° pitch angles. Their corresponding alignment indices, as determined through the NCP-criterion were 19° and 8°, respectively, which suggests that steeper pitch angles yield superior alignment. This proposed algorithm offers a novel way to implicitly define cellular alignment, with respect to various patterned biomaterial formats. This method may also offer an alternative, quantitative way to assess cellular alignment, which could offer improved predictive measures related to biological outcomes. Furthermore, the current algorithm is used for evaluating biomaterials in 2D; however, the NCP-criterion may be modified to accommodate 3D data, which will be important for understanding and quantifying regenerative outcomes in vivo.