Random distortion testing (RDT) addresses the problem of testing whether or not a random signal deviates by more than a specified tolerance from a fixed value. The test is non-parametric in the sense that the distribution of the signal under each hypothesis is assumed to be unknown. The signal is observed in independent and identically distributed (i.i.d) additive noise. The need to control the probabilities of false alarm and missed de- tection while reducing the number of samples required to make a decision leads to the SeqRDT approach. We show that under mild assumptions on the signal, SeqRDT will follow the properties de- sired by a sequential test. Simulations show that the SeqRDT approach leads to faster decision making compared to its fixed sample counterpart Block-RDT and is robust to model mismatches compared to the Sequential Probability Ratio Test (SPRT) when the actual signal is a distorted version of the assumed signal espe- cially at low Signal-to-Noise Ratios (SNRs).