We consider the simultaneous seat-inventory control of a set of parallel flights between a common origin and destination with dynamic customer choice among the flights. We formulate the problem as an extension of the classic multiperiod, single-flight “block demand” revenue management model. The resulting Markov decision process is quite complex, owing to its multidimensional state space and the fact that the airline’s inventory controls do affect the distribution of demand. Using stochastic comparisons, consumer-choice models, and inventory-pooling ideas, we derive easily computable upper and lower bounds for the value function of our model. We propose simulation-based techniques for solving the stochastic optimization problem and also describe heuristics based upon an extension of a well-known linear programming formulation. We provide numerical examples.