This demonstration presents a tool, called DustDoctor, for troubleshooting sensor data fusion systems in which data is combined from multiple heterogeneous sources to compute actionable information. Application examples include target detection, critical infrastructure monitoring, and participatory sensing. In such systems, the correctness of end results may become compromised for a variety of possible reasons, such as node malfunction, bugs, environmental conditions unfavorable to certain sensors, or assumption mismatches (such as use of incompatible units on different nodes of the same distributed computation). DustDoctor adapts algorithms borrowed from previous discriminative mining literature to analyze data fusion flow graphs, called provenance graphs, and isolate sources and conditions correlated with anomalous results. This information is subsequently used to isolate malfunctioning components or filter out erroneous reports. We demonstrate our approach on MicaZ motes, running a simple data collection application, where users are allowed to inject a variety of different emulated faults, leaving it to DustDoctor to find and isolate them to prevent contamination of fusion results.