A parallel algorithm for Kalman filtering with contaminated observations is developed. This algorithm is suitable for the parallel computer implementation allowing to treat dynamic linear systems with large number of state variables in a robust recursive way. The implementation is based on the square root version of the Kalman filter. It represents a great improvement over serial implementations reducing drastically computational costs for each state update and avoiding numerical instability problems.