An algorithm for gamma-ray identification applications has been developed and evaluated. The algorithm is based on a Fisher Linear Discriminant Analysis (FLDA) technique that generates loading coefficients that maximize the separation of a particular radionuclide from all the other radionuclides in a training library. Separate experimental data sets were obtained for the algorithms training data and for the performance evaluation. The algorithm was evaluated against a range of radionuclides and acquisitions times. An inverse square root relationship between the cluster standard deviation and its gross mean counts enabled the production of an adaptable threshold. The inverse square relationship between the Mahalanobis distance metric and the 137Cs standoff distance demonstrated a means to quantify the measured number of counts. The FLDA identification performance, for a number of threat radionuclides (including special nuclear materials), exceeded that of a commercially available peak search algorithm. The high sensitivity and specificity, of the FLDA algorithm, was maintained in low count situations. The poor performance for some radionuclides was attributed to the measured number of counts being below the minimal detectable limit. The FLDA algorithm has the potential to be used in gamma-ray identification applications and, in particular, count starved situations.