Model-based clustering takes a finite mixture based approach to the problem of finding groups in a set of data. In finite mixtures, a probability density function is modeled as a weighted sum of component densities. The Expectation-Maximization (EM) algorithm is then used to estimate the parameters of the mixture. With this methodology, we hypothesize that each component density is representative of a cluster or group. Several issues must be addressed when this technique is used. How many clusters or component densities should be used? What is a good starting point for the EM algorithm? What 'form' should the component densities take? How can we assess the results of the clustering? This presentation is tutorial in nature and will show the model-based clustering methodology. The concepts will be demonstrated using a MATLAB Model-Based Clustering Toolbox.