In order to understand the connectivity of neuronal networks, their constituent neurons should ideally be studied in a common framework. Since morphological data from physiologically characterized and stained neurons usually arise from different individual brains, this can only be performed in a virtual standardized brain that compensates for interindividual variability. The desert locust, Schistocerca gregaria, is an insect species used widely for the analysis of olfactory and visual signal processing, endocrine functions, and neural networks controlling motor output. To provide a common multi-user platform for neural circuit analysis in the brain of this species, we have generated a standardized three-dimensional brain of this locust. Serial confocal images from whole-mount locust brains were used to reconstruct 34 neuropil areas in ten brains. For standardization, we compared two different methods: an iterative shape-averaging (ISA) procedure by using affine transformations followed by iterative nonrigid registrations, and the Virtual Insect Brain (VIB) protocol by using global and local rigid transformations followed by local nonrigid transformations. Both methods generated a standard brain, but for different applications. Whereas the VIB technique was designed to visualize anatomical variability between the input brains, the purpose of the ISA method was the opposite, i.e., to remove this variability. A novel individually labeled neuron, connecting the lobula to the midbrain and deutocerebrum, has been registered into the ISA atlas and demonstrates its usefulness and accuracy for future analysis of neural networks. The locust standard brain is accessible at http://www.3d-insectbrain.com.