There usually exists significant correlation between such radioactive para-meters as K, U and Th in airborne gamma-ray spectrometric data, whichcovers up some useful information. Using principal component analysis, we canextract a few new variables uncorrelated to each otber from a number ofhighly correlated observed data, thus getting rid of this trouble.The interpretation of airborne gamma-ray spectrometric data by usingprincipal component analysis in the middle of CDM basin shows that thismethod can be used to help lithologic and structural mapping, recognize envi-ronments favorable for uranium and sylvite deposition, establish minerogenicmodels for these two types of deposit, and make minerogenic perspective pro-gnosis for them. It is hence conceivable that principal component analysis islikely to be a new potential technique in the interpretation of airborne gamma-ray spectrometric data.