Similarity search in time series databases has been the focus of a lot research. It is non-trivial problem due their peculiar structure, high dimensionality [6], noise, and high feature correlation; it is also the heart of many data mining applications, like clustering, classification, rule discovery and query by content. In general, similarity has been measured with the Euclidean metric, but the dimensionality curse problem remains [5]. Basically, there are four mayor techniques that have been proposed in order to reduce the dimensionality of the data [1], Singular Value Decomposition (SVD), the Discrete Fourier Transform (DFT), the Discrete Wavelets Transform (DWT), and the Piecewise Aggregate Approximation PAA [2]. In this work we use the Dynamic Time Warping (DTW) algorithm and PAA dimensionality reduction technique as a new approach to find the similarity between seismological signals.