In this study, a target detection algorithm was proposed for using hyperspectral imagery. The proposed algorithm is designed to have minimal processing time, low false alarm rate, and flexible threshold selection. The target detection procedure can be divided into two steps. Initially, candidates of target pixel are extracted using matching ratio of spectral pattern that can be calculated by spectral derivation. Secondly, spectral distance is computed only for those candidates using Euclidean distance. The proposed two-step method showed lower false alarm rate than the Euclidean distance detector applied over the whole image. It also showed much lower processing time as compared to the Mahalanobis distance detector.