In surficial geochemical exploration, scholars have often depend on simple statistical means or single-element concentration to investigate the relationship between elements in surface media and buried orebodies. In this study, a multivariate data analysis technique was used to explore the geochemistry of surfacestream sediments filling the main course of Wadi Haimur and its creeks. In this study, PCA analysiscombined with cluster analysis was not only able to identify relationships between elements that could beassociated with regional geologic features but was also better able to recognize type of mineralization thatwere not apparent when examining the data with univariate methods. Sixty-five stream sediment sampleswere collected in the vicinity of basement rocks of Late-Proterozoic age, and analyzed for their As, Bi,Cu, Mo, Ni, Pb, Sb, Sn, W, Ba, Cr, Fe, Mn, Rb, Sr, Ti and Zn contents. In the present study, statistical clusterand Principal Component Analysis (PCA) analyses revealed useful explanation of the given data. Theyclassify variables into two main groups: the first contains the rock forming elements, while the second showsprobably mineralized elements group (ore forming elements) beside the identification of geochemicalsignature of two possibly gold deposits.