The covariation of short-time period returns between securities plays an important role in many area of finance. Under the wide availability of high frequency financial data, realized covariation, as an ex-post measure of the covariation, can accurately estimate the quadratic covariation. However, the realized covariation fails to work when the multiple records appear. In this paper, we propose an estimator of integrated covariation, which is robust to the high frequency data containing multiple records. Consistency of the estimator and central limit theorem have been established. Moreover, several extensions which make the estimator available to different types of high frequency data are also considered. Simulation study confirms the performance of the estimator.