An interactive image query system based on multiple seed images is proposed in this work. With this proposed system, the query can be refined so that the meaning of similarity becomes clear along the query process. A particular way to achieve interactive query is implemented, i.e. adaptive filtering with multiple low-level indexing features based on user's feedback. The proposed query system consists of the following building blocks. First, browsing, image sketching and feature editing are employed for query formation input. The combination of the three methods provides high flexibility for users to get desired query images. In the system, images can also be selected from the candidate image set. By using a query set composed of multiple seed images instead of a single image, we can improve the query performance with more accurate similarity information. Two key procedures, initial guess and further refinement, are utilized to achieve high efficient query. At the initial stage, we try to expand the candidate image set to include as many features as possible. In the refinement process, users are able to initiate a more complex filtering strategy by using the feedback. The relative weighting of different features is further decided. Extensive examples are used to illustrate the proposed interactive query process and the corresponding retrieval performance.