Consider a job-shop or batch-flow manufacturing system in which new jobs are introduced only as old ones depart, either because of physical constraints or as a matter of management policy. Assuming that there is never a shortage of new work to be done, the number of active jobs remains constant over time, and the system can be modeled as a kind of closed queueing network. With manufacturing applications in mind, we formulate a general closed network model and develop a mathematical method to estimate its steady-state performance characteristics. A restrictive feature of our network model is that all the job classes that are served at any given node or station share a common service time distribution. Our analytical method, which is based on an algorithm for computing the stationary distribution of an approximating Brownian model, is motivated by heavy traffic theory; it is precisely analogous to a method developed earlier for analysis of open queueing networks. The required inputs include not only first-moment information, such as average product mix and average processing rates, but also second-moment data that serve as quantitative measures of variability in the processing environment. We present numerical examples that show that system performance is very much affected by changes in second-moment data. In these few numerical examples, our estimates of average throughput rates and average throughput times for different product families are generally accurate when compared against simulation results.