The real power of performance models becomes evident when they are used for predictive purposes. In performance engineering, models are essential because of their ability to predict adequately the performance of a particular system under different workloads. Real-life systems experience a wide variety of customers (e.g., experienced heavy users, novices, web surfers, e-mail users, bank transactions) with different resource usage profiles. Actual workloads do not tend to be a single class of homogeneous customers. Typically, each customer differs from every other customer. However, it is impractical to model each customer's individual idiosyncrasies exactly. Rather, customers are grouped into classes of similar behaviors, which are then represented in the model as the average behavior of the class and the customer population of the class. Therefore, techniques are needed that solve multiple-class performance models. This chapter provides MVA-based algorithms for solving open and closed product-form queuing network models with multiple classes. The techniques include exact and approximate solutions. The MS Excel workbooks OpenQN.XLS and ClosedQN.XLS implement the open and closed multiclass QN solution techniques described in this chapter, respectively.