An important step in chart image understanding is to identify the type of the input image so that corresponding interpretation can be performed. In this paper, we model the chart image classification as a multiple-instance learning problem. A chart image is treated as a bag containing a set of instances that are graphical symbols. For both training and recognition, shape detection is performed and general shape descriptors are used to form feature vectors. For the training images, the correlation factor (CF) of each shape is calculated for each chart type. The learnt CFs are then used to estimate the type of a new input image. Comparing with traditional multiple-instance learning algorithms, we allow negative examples to be less restrictive and hence easier to provide. Using our method, both the type and the data components of the chart image can be obtained in one-pass. The experimental results show that our approach works reasonably well.