Two other projection matrices, used in the solution of data reconciliation problems, are described in this short note. The first matrix projection introduced is straightforward to compute, is idempotent and can be easily updated when a measurement is deleted or removed from the problem. The second projection matrix, while although being more numerically intensive in its computation than the first, may prove superior when ill-behaved or ill-conditioned systems are reconciled given that it employs the very numerically stable singular value decomposition. Two small examples, one non-linear and the other linear, are presented which demonstrate the use of the new projection matrices and serve as a comparison to the well-known matrix projection of Crowe, Garcia, & Hrymak (1983).