AbstractThis paper examines a class of sampled systems where the data collection is disturbed by external reasons and several samples are missing. Common data logging problems such as, instrumentation failure, unpredicted power failure, etc,, can be solved by applying theproposed signal processing algorithms.The technique used is based on modeling the process with an auto regressive moving average (ARMA) filter. Identifying the parameters of the filter and using the filter to estimate the missing observations. Simulation results indicate the applicability of the algorithms in a microcomputer environment, with limited computationa resources. The algorithms give satisfactory results in case of burst missing data.