By using multi-electrode arrays or optical imaging, investigators can now record from many individual neurons in various parts of nervous systems simultaneously while an animal performs sensory, motor or cognitive tasks. Given the large multidimensional datasets that are now routinely generated, it is often not obvious how to find meaningful results within the data. The analysis of neuronal-population recordings typically involves two steps: the extraction of relevant dynamics from neural data, and then use of the dynamics to classify and discriminate features of a stimulus or behavior. We focus on the application of techniques that emphasize interactions among the recorded neurons rather than using just the correlations between individual neurons and a perception or a behavior. An understanding of modern analysis techniques is crucially important for researchers interested in the co-varying activity among populations of neurons or even brain regions.