Background: There is a niche for developing an ideal pre-operative scoring system for predicting mortality in patients undergoing colorectal surgery. Biochemistry and Haematology Outcome Models (BHOM) adopt the approach of using a minimum dataset to model outcome following colorectal cancer surgery, a concept previously shown to be feasible after index arterial operations. Methods: Predictive binary logistic regression models (a mortality and morbidity model) were developed on 704 patients who underwent colorectal cancer surgery over a six-year period in a UK district general hospital. The outcome variables measured were 30 day post-operative mortality, and morbidity (defined as major/minor leak, abscess, bleeding or obstruction). Hosmer-Lemeshow goodness of fit statistics and frequency tables compared the predicted versus the reported number of deaths. Discrimination was quantified using the c-index. Results: The dataset consisted of 573 elective cases and 131 non-elective interventional cases. The overall mean predicted risk of death was 7.79% (50 cases). The actual number of reported deaths was also 50 (χ2 = 1.331, d.f.=4, p-value = 0.856; no evidence of lack of fit). For the mortality model, the predictive c-index was = 0.810. The morbidity model had less discriminative power, however, there was no evidence of lack of fit (χ2 = 4.198, d.f.=4, p-value = 0.380, c-index = 0.697). Conclusions: The CR BHOM mortality model suggests good discrimination (c-index >0.8) and uses only a minimal number of variables. However, the model needs to be tested on independent datasets from different geographical locations.