Multiple Sclerosis (MS) associated cognitive impairment is believed to be mostly connected with damage to gray matter. The contribution of white matter is still poorly understood. We aim to examine the relationship between cognition and white matter tracts among relapsing remitting MS (RRMS) patients. Thirty RRMS patients were selected undergo the (3-seconds-interstimulus-interval paced auditory serial addition test) PASAT-3, the (symbol digit modalities test (SDMT) and full-brain MRI scans on a SIEMENS 3 Tesla Verio scanner. Diffusion Tensor Imaging (DTI) parameters, such as fractional anisotropy (FA) and mean diffusivity (MD) were examined in 37 white matter (WM) tracts. WM tracts were selected from the association pathways, projection pathways, commissural pathways by applying Human Connectome project (HCP)842 tractography atlas after DTI data reconstruction and registration to HCP1065 diffusion template in DSI Studio (version March 2021) In SPSS v26, Spearman's rank correlation analysis was used to examine the connection between DTI WM tracts and cognitive scores. The power of the study was increased by using false discovery rate (FDR) software. The mean scores on the PASAT-3 and SDMT were 31.5±12.8 and 46.9±16.7 respectively. Better cognitive performance was correlated to higher FA values, while lower cognitive function was correlated to higher MD values. There was a positive correlation between FA values in the right medial lemniscus and superior cerebellar peduncle and SDMT scores (p 0.05). Additionally, there was a trend for significance between the FA values in the left corticothalamic tract and SDMT scores. MD values in the superior cerebellar peduncle, left arcuate Fasciculus and left extreme capsule were negatively correlated with SDMT scores (p<0.05). PASAT-3 scores were negatively correlated with MD values in the right cerebellum, however, there was no significant correlation between PASAT-3 and FA values. White matter tracts, particularly the superior cerebellar peduncle, contribute to the cognitive impairment in RRMS. Larger sample sizes for longitudinal research are necessary.