This paper investigates the subsequence searching problem under time warping in sequence databases. Time warping enables to find sequences with similar changing patterns even when they are of different lengths. Our work is motivated by the observation that subsequence searches slow down quadratically as the total length of data sequences increases. To resolve this problem, we propose the Segment-Based Approach for Subsequence Searches (SBASS), which modifies the similarity measure from time warping to piece-wise time warping and limits the number of possible subsequences to be compared with a query sequence. For efficient retrieval of similar subsequences, we extract feature vectors from all data segments exploiting their mono-tonically changing properties, and build a multi-dimensional index such as R-tree or R∗-tree. Using this index, queries are processed with four steps: 1) index filtering, 2) feature filtering, 3) successor filtering, and 4) post-processing. The effectiveness of our approach is verified through experiments on synthetic data sets.