Multispectral narrow band imaging (NBI) represents a promising screening tool for oropharyngeal cancer (OPC) due to its selective enhancement of tumor vasculature. However, most implementations of NBI for cancer screening have relied on qualitative observations of mucosa (e.g., presence of capillary loops), rather than quantitative measures based on image features. This preliminary study was designed to determine if specific narrow band signal characteristics of tumor vasculature may be used to train a machine learning algorithm for quantitative OPC detection. Adult patients with no tumor (5) and biopsy-proven oropharyngeal squamous cell carcinoma (25) were recruited, and consent was obtained to examine the oropharynx by endoscopy under white light endoscopy (WLE) and narrow band imaging (NBI). De-identified WLE and NBI laryngoscopy videos were then read frame-by-frame into Matlab 2011a and sampled regions of mucosa were processed to extract color (a, b values in L*a*b image space) and texture (pixel entropy) information. Color and texture image features of tumor and non-tumor sides for 5 patients (4 tumor, 1 healthy) were used to train a Naïve Bayesian classifier for the remaining 25 (21 tumor, 4 healthy), and performance under WB and NBI was compared by ROC analysis. Compared to WLE, mNBI significantly enhanced the performance of a Naïve Bayesian classifier trained on low-level image features of oropharyngeal mucosa (76.6% AUC vs 50.5% for WLE). These findings suggest that automated clinical detection of oropharyngeal carcinoma could be used to enhance surgical vision, improve early diagnosis, and allow for high-throughput screening.