This chapter introduces Gibbs (including Markov) point process models, which are flexible and natural models for point patterns with dependence between points. We explain the basic concepts, and show how to build a Gibbs model appropriate to the data, to fit the model to the data, to interpret the results, and to identify weaknesses in the analysis.