Abstract Flip chip technology has been extensively used in high density electronic packaging over the past decades. With the decrease of solder bumps in dimension and pitch, defect inspection of solder bumps becomes more and more challenging. In this paper, an intelligent diagnosis system using the scanning acoustic microscopy (SAM) is investigated, and the fuzzy support vector machine (F-SVM) algorithm is developed for solder bump recognition. In the F-SVM algorithm, we apply a fuzzy membership to input feature data so that the different input features can make different contributions to the learning procedure of the network. It solves the problem of feature data aliasing in the traditional SVM. The SAM image of flip chip is captured by using an ultrasonic transducer of 230 MHz. Then the segmentation of solder bumps is based on the gradient matrix of the original image, and the statistical features corresponding to every solder bump are extracted and adopted to the F-SVM network for solder bump classification and recognition. The experiment results show a high accuracy of solder defect recognition, therefore, the diagnosis system using the F-SVM algorithm is effective and feasible for solder bump defect inspection.