Traditional field-level wood identification can play an important role in combating illegal logging by helping law enforcement officers determine which wood shipments should be detained and submitted for full forensic analysis. Wood identification in general, whether in the field or the laboratory, is based on human-mediated recognition of macroscopic biological patterns in wood. Recognition of biological structures is complex, often subjective, difficult to quantify, and requires costly and intensive training. To objectively quantify the macroscopic biological structure of wood and to eliminate the expensive training of field personnel, Forest Service researchers developed the Xylotron, a machine-vision system using biology-independent signal processing algorithms (wavelets) to form a reference database of woods. This approach removes human subjectivity from the data acquisition process, but requires control over objective factors influencing the image, especially the optical properties of the system. To establish this control, the researchers developed the Xyloscope, a custom-designed system to capture images of wood for the Xylotron. Currently, the Xylotron is able to identify over 150 neotropical woods. The system makes correct identifications with greater than 80 percent accuracy, better than that of trained field agents. Currently the system is a prototype status and is being tested in five other laboratories around the world.