The Rio Grande River of west Texas contains by far the largest infestation of saltcedar (Tamarix spp.) in Texas. The objective of this study was to evaluate airborne hyperspectral imagery and different classification techniques for mapping saltcedar infestations. Hyperspectral imagery with 102 usable bands covering a spectral range of 475 to 845 nm was acquired from two sites along the Rio Grande in west Texas in December 2003 and 2004 when saltcedar was undergoing color change. The imagery was transformed using minimum noise fraction and then classified using six classifiers: minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper, mixture tuned matched filtering, and support vector machine (SVM). Accuracy assessment showed that overall accuracy varied from 71% to 86% in 2003 and from 80% to 90% in 2004 for site 1 and from 60% to 76% in 2003 and from 77% to 91% in 2004 for site 2. The SVM classifier produced the highest overall accuracy, as well as the best user’s and producer’s accuracies for saltcedar among the six classifiers. The imagery taken in early December 2004 provided better classification results than that in mid-December 2003. Change detection analysis based on the classification maps quantified the class changes between the two years. These results indicate that airborne hyperspectral imagery incorporated with image transformation and classification techniques can be a useful tool for mapping saltcedar infestations.