During storage, grain can experience significant degradation in quality due to a variety of physical, chemical, and biological interactions. Most commonly, these losses are associated with insects or fungi. Continuous monitoring and an ability to differentiate between sources of spoilage are critical for rapid and effective intervention to minimize deterioration or losses. Therefore, there is a keen interest in developing a straightforward, cost-effective, and efficient method for monitoring of stored grain. Sensor arrays are currently used for classifying liquors, perfumes, and the quality of food products by mimicking the mammalian olfactory system. The use of this technology for monitoring of stored grain and identification of the source of spoilage is a new application, which has the potential for broad impact. The main focus of the work described herein is on the fabrication and optimization of a carbon black (CB) polymer sensor array to monitor stored grain model volatiles associated with insect secretions (benzene derivatives) and fungi (aliphatic hydrocarbon derivatives). Various methods of statistical analysis (RSD, PCA, LDA, t test) were used to select polymers for the array that were optimum for distinguishing between important compound classes (quinones, alcohols) and to minimize the sensitivity for other parameters such as humidity. The performance of the developed sensor array was satisfactory to demonstrate identification and separation of stored grain model volatiles at ambient conditions.