Distributed optical fiber sensors are measuring tools whose potential related to the civil engineering field has been discovered in the latest years only (reduced dimensions, easy installation process, lower installation costs, elevated reading accuracy, and distributed monitoring). Yet, what appears clear from numerous in situ distributed optical fiber sensors monitoring campaigns (bridges and historical structures among others) and laboratory confined experiments is that optical fiber sensors monitorings have a tendency of including in their outputs a certain amount of anomalistic readings (out of scale and unreliable measurements). These can be both punctual in nature and spread over all the monitoring duration. Their presence strongly affects the results both altering the data in its affected sections and distorting the overall trend of the strain evolution profiles, thus the importance of detecting, eliminating, and substituting them with correct values. Being this issue intrinsic in the raw output data of the monitoring tool itself, its only solution is computer-aided post-processing of the strain data. This article discusses different simple algorithms for getting rid of such disruptive anomalies using two methods previously used in the literature and a novel polynomial-based one with different levels of sophistication and accuracy. The viability and performance of each are tested on two study case scenarios: an experimental laboratory test on two reinforced concrete tensile elements and an in situ tunnel monitoring campaign. The outcome of such analysis will provide the reader with both clear indications on how to purge a distributed optical fiber sensors-extracted data set of all anomalies and on which is the best-suited method according to their needs. This marriage of computer technology and cutting edge structural health monitoring tool not only elevates the distributed optical fiber sensors viability but also provides civil and infrastructures engineers a reliable tool to perform previously unreachable levels of accuracy and extension monitoring coverage.