Detection of architectural distortion (AD) in mammograms is one of imp ortant approaches in breast cancer diagnosis.Using support vector machine (SVM) to detect AD can achieve hi gh accuracy rate,but it is also with slow speed,making it not suitable for clini cal application.To solve the above problems,a method to detect AD in mammograms based on relevance vector machine (RVM) is proposed.Firstly,th e discrete wavelet transform is applied to extract features in region of interest (ROI).Then the c ross validation (CV) method is used to determine the optimum type and parameters of RVM kernel function.Lastly,RVM is applied to classify the test samples to obtain the detection results of AD.The proposed method is evaluated on mammograms from the mammographic image analysis society (Mini-MIAS) and those from the breast cancer of Peking University People′s Hospital.The results show that compared with SVM method,t he proposed method achieves essentially the same sensitivity with much higher speed of detectio n,which can shorten the detection time of AD more than 90%.The proposed method is more applicable f or mammograms with different characteristics of both oriental and occidental wom en.