Fibres comparison in forensic science play a significant role in solving different crimes. Worldwide the most common textile fibre materials (fabrics) found at crimes scenes are cotton, polyester, denim, polypropylene, polycotton, and viscose. These fabrics are the focus of this study. The textile fabrics were examined by two handheld near-infrared (NIR) spectrometers, SCIO® by Consumer Physics and NIRscan Nano by Texas Instruments, for in situ comparisons of fibres, demonstrating capability at a crime scene. Spectral differences were apparent between both spectrometers due to the complementary wavelengths, SCIO interrogates the third overtone region (740 – 1070 nm) and NIRscan Nano interrogates the first and second overtone regions (900 – 1700 nm). A SCIO and NIRscan Nano data were pre-processed to eliminate noise and smooth the data for input to machine learning classifiers. The data were pre-processed and modelled by PRFFECTv2 software, and showed good predictive accuracy, with accuracy, sensitivity and specificity in the range 78-100% for the best binary classification models (one class versus others) and within the range 65-100% for the best multi-class classification models. This paper presents for the first time the use of small handheld spectrometers coupled with the Random Forest (RF) algorithm to classify fibre material for forensic comparison purposes in a fast, rapid, and non-destructive manner that is ideally suited for direct analysis at the crime scene.