<jats:p>Environmental context Microplastics are of increasing environmental concern following reports that they occur worldwide from the arctic to the deep sea. However, a reliable methodology that facilitates an automated measurement of abundance and identity of microplastics is still lacking. We present an analytical protocol that applies focal plane array detector-based infrared imaging of microplastics enriched on membrane filters applicable to investigations of microplastic pollution of the environment. Abstract The pollution of the environment with microplastics (plastic pieces &lt;5 mm) is a problem of increasing concern. However, although this has been generally recognised by scientists and authorities, the analysis of microplastics is often done by visual inspection alone with potentially high error rates, especially for smaller particles. Methods that allow for a fast and reliable analysis of microplastics enriched on filters are lacking. Our study is the first to fill this gap by using focal plane array detector-based micro-Fourier-transform infrared imaging for analysis of microplastics from environmental samples. As a result of our iteratively optimised analytical approach (concerning filter material, measuring mode, measurement parameters and identification protocol), we were able to successfully measure the whole surface (&gt;10-mm diameter) of filters with microplastics from marine plankton and sediment samples. The measurement with a high lateral resolution allowed for the detection of particles down to a size of 20 μm in only a fractional part of time needed for chemical mapping. The integration of three band regions facilitated the pre-selection of potential microplastics of the ten most important polymers. Subsequent to the imaging the review of the infrared spectra of the pre-selected potential microplastics was necessary for a verification of plastic polymer origin. The approach we present here is highly suitable to be implemented as a standard procedure for the analysis of small microplastics from environmental samples. However, a further automatisation with respect to measurement and subsequent particle identification would facilitate the even faster and fully automated analysis of microplastic samples.</jats:p>