In this paper a real-time cooperative path decision algorithm for UAV surveillance is proposed. The surveillance mission includes multiple objectives: i) navigate the UAVs safely in a hostile environment; ii) search for new targets in the surveillance region; iii) classify the detected targets; iv) maintain tracks on the detected targets. To handle these competing objectives, a layered decision framework is proposed, in which different objectives are deemed relevant at different decision layers according to their priorities. Compared to previous work, in which multiple objectives are integrated into a single global objective function, this layered decision framework allows detailed specification of the desired performance for each objective and guarantees that an objective with high priority will be better satisfied by eliminating possible compromises from other less important ones. In addition, specific path decision strategies that are suited to the individual objectives can be used at different decision layers. An important objective of the path decision algorithm is to navigate the UAV safely in the hostile environment. To achieve this, it is shown necessary to increase the time horizon of the path decisions. In order to overcome the computational explosion of an optimal multi-step look-ahead path decision strategy, a Rollout Policy is proposed. This policy has moderate complexity and, when used in the layered decision framework, it is able to find safe paths effectively and efficiently. When the number of UAVs is large, the formation of UAV decision groups based on a nearest neighbor rule is proposed to control the complexity of the path decision algorithm. Further flexibility of assigning different objectives to the UAVs is also discussed. Simulation results show that the proposed path decision algorithm can guide the group of UAVs efficiently and safely for the multi-objective surveillance mission.