Predicting financial distress among SMEs can have a significant impact on the economy as it serves as an effective early warning signal. The study develops distress prediction models combining financial, non-financial and governance particularly ownership and board structures, on the likelihood of financial distress by using the logit model. The final sample for the estimation model consists of 172 companies with 50% non-failed cases and 50% failed cases for the period from 2000 to 2012. The prediction models perform relatively well especially Model 3 that incorporates governance, financial and nonfinancial variables, with an overall accuracy rate of 93.6% and 91.2% in the estimated sample and holdout sample respectively. This evidence shows that the models serve as effective early warning signals which are beneficial for monitoring and evaluation purposes. Controlling shareholder, number of directors and gender of managing director are found to be significant predictors of financially distressed SMEs.