The usual practice in Statistical Process Monitoring techniques assumes that the data distribution is known and the related parameters are accurately estimated. In practice, the underlying distribution and its parameters are rarely known, and control charts need to be constructed with parameters being estimated. Such issues have recently received an increasing attention in evaluating the properties of both parametric and nonparametric charts. However, the same study is seldom conducted for the control charts based on the data-driven tools. In this paper, we investigated the in-control performance of a nonparametric control chart based on the Support Vector Data Description (SVDD) theory. More specifically, we discuss the conditional effect of the training Phase-I samples on the Phase-II efficiency when different distributions are considered. Simulation results show that the conditional performance of the SVDD-based chart can be strongly affected by the Phase-I samples. It this situation, adjusted control limits with a specific number of available training sample is suggested.