Wireless sensor network applications typically integrate, within the same network, a variety of sensing devices including those for imaging, sound and temperature. In these settings, multiple flows of packets with different requirements in terms of transmission rates, bandwidth and jitter demands may be initiated towards the sink. Uncontrolled introduction of traffic from sources can cause network overload in areas of the network where the paths of the different flows interfere with each other. Such interference effects may result in congestion which leads to high packet loss and excessive delays. In this paper, we present CoBRA, a framework which incorporates distributed, cluster-based mechanisms to address the problem of congestion by enforcing rate control, for supporting multiple classes of traffic in sensor networks. Towards this goal, CoBRA periodically estimates the collective traffic load and, based on the current conditions, allocates and adjusts rates to sources on per-cluster bases. While doing so, CoBRA takes into consideration interference effects and rate requirements of concurrent flows. We have applied two different rate allocation policies using our framework and, through extensive simulation results we demonstrate its feasibility, effectiveness and performance advantages over traditional approaches.