With the advances in hardware technologies, embedded and Edge devices are now able to offer sufficient memory and computational power to accommodate light-weight machine-learning (ML) classifiers. However, due to the intensive code optimization and summarization in the design phase, the reliability of light-weight ML applications is at risk. In this paper, we study the reliability of three prototypical light-weight ML applications against the well-known control flow (CF) errors. We have injected a total of 66,156 CF errors into Bonsai, ProtoNN, and TensorFlow Lite ML applications running on the Arduino board. Based on the results obtained from the error-injections, we found that CF errors could affect either the functionality or the classification accuracy of the ML-based inference as the embedded application. We conclude that making a single decision only after a long sequence of computations/branches may be more error-prone. This issue can be addressed by combining the inferences of intermediate nodes in the chain to obtain the final classification decision.