Palmer amaranth is a troublesome weed in modern day agriculture. Timely identification, along with adoption of site-specific weed management practices, will enable farmers to reduce Palmer amaranth control costs and improve efficacy. The feasibility of collecting hyperspectral imagery to identify Palmer amaranth and soybean was evaluated in the greenhouse and field. Hyperspectral images were collected across 224 spectral bands onPalmer amaranth and soybean twice weekly from the one to three-leaf growth stage in three different runs (28 replications per run) temporally separated in a greenhouse. Partial least squares-discriminant analysis and soft independent modelling of class analogy models were developed to identify Palmer amaranth and soybean plants and had cumulative variations of 60% and 85%, and predictive abilities of 60% and 82%, respectively. This study concluded that hyperspectral imaging could be a potential tool to decipher Palmer amaranth from soybean plants.