Microprocessor clock rates-which for three decades doubled about every 18 months-have essentially stopped increasing. Instead, the number of processor cores (identical processing units capable of all usual microprocessor functions) in a microprocessor is increasing exponentially with time. In order to increase performance as the number of cores increase, a measurement analysis software will have to take advantage of this parallelism. The objectives of this paper are to study one example of a measurement analysis having serial dependencies among the input data and to show that there is a practical parallel algorithm despite the data dependencies within the measured time series. The measurement analysis studied is transition localization in digital signals. A parallel scan-type algorithm is presented. The results of applying the parallel algorithm on both synthetic data and actual measured data are presented, and the speedup obtained on a twenty-four core computer analyzed. The parallel method produces exactly the same measurement results, bit for bit, as the original serial method. It is argued that what is desired for this and many other measurement processing algorithms is scalability in throughput with number of cores. Such scalability is achieved by the proposed algorithm, with throughput up to about a dozen cores.