This paper deals with applications of the vector triggering Random Decrement technique. This technique is new and developed with the aim of minimizing estimation time and identification errors. The theory behind the technique is discussed in an accompanying paper. The results presented in this paper should be regarded as a further documentation of the technique. The key point in Random Decrement estimation is the formulation of a triggering condition. If the triggering condition is fulfilled a time segment from each measurement is picked out and averaged with previous time segments. The final result is a Random Decrement function from each measurement. In traditional Random Decrement estimation the triggering condition is a scalar condition, which should only be fulfilled in a single measurement. In vector triggering Random Decrement the triggering condition is a vector condition. The advantage of this new approach should be a reduction in estimation time without a significant loss of accuracy, since the vector triggering conditions ensure cross information between the measurements in the Random Decrement functions. The different problems with this technique is highlighted in two examples. A simulation study of a 4 degree of freedom system and the identification of a laboratory bridge model, both<br/>loaded by white noise, is made.