Background: Technical and Vocational Education and Training (TVET) refers to a range of learning experiences which are relevant to the world of work and contemporary workplaces. The core role of Technical and Vocational Education and Training (TVET) is developing professional skills in youth; equip them with basic knowledge and scientific principles to prepare them for work. Based on this vital role it is expected that youth enroll in TVET for selfreliance. However, the preliminary data from Trans-Nzoia County TVET office showed that there is low enrolment in TVET institutions prompting this study to determine the influence of infrastructural facilities on access to TVET. Materials and Methods: The study adopted The Production Function Theory which suggests that an increase in access to TVET is dependent on the inputs. The scope of the study was 28 County Vocational Training Centers with 161 trainers and 2931 trainees. Stratified random sampling was used in sampling out the VTCs across Trans-Nzoia County where 15 VTCs were sampled. The sample size had 464 respondents; 1 VTC director, 15 head of VTCs, 108 trainers and 340 trainees. Purposive sampling was used to sample VTC heads & director while simple random sampling was used to obtain trainers and trainees. Questionnaires and interview guide was used to collect data. The validity of the research instruments was ascertained through expert judgment. The reliability of the research instruments was determined using the test-retest method. The instruments produced reliability coefficient of 0.82 hence the tools were considered reliable. Results: Quantitative data was analyzed using inferential statistics; PPMC at α = 0.05 and simple regression analysis was used to test the hypothesis. The Pearson product moment correlation index obtained on the relationship between Classrooms and Access to TVET was the highest (r= 0. 983, ρ<0.0001) at α= 0.05),). However, Staff quarters (r = -0.047, ρ= 0.437) at α= 0.05) did not correlate with access to TVET. The coefficient of determination R2 value was 0.791 for regression analysis. This showed that 79.1 per cent of the dependent variable can be predicted by the independent variable. The F-statistics produced (F = 785.056) was significant at 5 per cent level (ρ<0.0001), thus confirming that at least one of the predictors was useful for predicting access to TVET. Conclusion: The study found out that the availability of infrastructure in VTCs was inadequate. The study recommends that infrastructural facilities be addressed so as to increase access to TVET. These findings will assist the County Government, Ministry of Education and donors on priority areas of funding to increase access to TVET.