Microplastics are defined as microscopic plastic particles in the range from few µm and up to 5 mm. These small particles are classified as primary microplastic when they are manufactured in this size range, whereas secondary microplastics arise from the fragmentation of larger objects. Microplastics are a widespread emerging pollutant and investigations are underway to determine potential harmfulness to biota and human health. However, progress is hindered by the lack of suitable analytical methods for rapid, routine and unbiased measurements. This work aims to develop an automated analytical method for the characterization of small microplastic (< 100 µm) using micro Fourier Transform Infrared (µ-FTIR) hyperspectral imaging and machine learning tools. Partial least squares discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) models were evaluated, applying different data pre-processing strategies for classification of nine of the most common polymers produced worldwide. The hyperspectral images were also analyzed to quantify particle abundance and size automatically. PLS-DA presented a better analytical performance in comparison with SIMCA models with higher sensitivity, sensibility and lower misclassification error. PLS-DA was less sensitive to edge effects on spectra and poorly focused regions of particles. The approach was tested on a seabed sediment sample (Roskilde Fjord, Denmark) to demonstrate the method efficiency. The proposed method offers an efficient automated approach for microplastic polymer characterization, abundance numeration and size distribution with substantial benefits for methods standardization.