Process control charts are widely used to display sample data from a process for purposes of determining whether a process is in control, for bringing an out-of-control process into control, and for monitoring a process to make sure that it stays in control. The usual practice in maintaining a control chart is to take samples from the process at fixed length sampling intervals. This research investigates the modification of the standard practice where the sampling interval or time between samples is not fixed but can vary depending on what is observed from the data. Variable sampling interval process control procedures are considered for monitoring the outcome of a production process. The time until the next sample depends on what is being observed in the current sample. Sampling is less frequent when the process is at a high level of quality and vise versa. Properties such as the average number of samples until signal, average time to signal and the variance of the time to signal are developed for the variable sampling interval Shewhart and cusum charts. A Markov chain is utilized to approximate the average time to signal and the corresponding variance for the cusum charts. Properties of the variable sampling interval Shewhart chart are investigated through Renewal Theory and Markov chain approaches for the cases of a sudden and gradual shift in the process mean respectively. Also considered is the case of a shift occurring in the time between two samples without the simplifying assumption that the process mean remains the same from time zero onward. For such a case, the adjusted time to signal is developed for both the Shewhart and cusum charts in addition to the variance of the adjusted time to signal. Results show that the variable sampling interval control charts are considerably more efficient than the corresponding fixed sampling interval control charts. It is preferable to use only two sampling intervals which keeps the complexity of the chart to a reasonable level and has practical implications. This feature should make such charts very appealing for use in industry and other fields of application where control charts are used. / Ph. D.