The treatment and handling of large quantities of time series sensor data is a particular challenge for the sport science community. Significant quantities of data can be generated during routine training sessions that involve multi-sensor monitoring on multiple limb segments. Whilst sensor devices are commonplace, the data formats, available sensors and acquisition rates vary considerably. Additionally sensor fusion is of increasing interest where multiple data sets from multiple sources are required to be combined. In this paper we present a set of tools that have been developed over the last 5 years to help meet this emerging challenge. The tools are based on the popular computational environment Matlab, which allows rapid customisation of routines, together with complex analysis and visualisation tools to be used by technical and non-technical researchers alike. Using these developed tools data gained from a variety of sources (including video) can be combined together, visualised and processed using over 50 processing and visualisation tools. The toolbox is designed to automatically annotate data sets to keep track of signal processing steps and maintain original data source integrity. It can also be easily extended and customised for individual applications. At its core is the 'athdata' data structure, which can accommodate multiple channels and kind of data at a variety of sample rates, annotations and unlimited processing steps. A sample import tool has been developed for users to easily apply the toolbox to their own data sets and real-time streaming of data into the toolbox is also possible. When adopted as a research team tool it facilitates the sharing of developed processing and visualisation steps, it also enables a faster path to application for new researchers joining any team.