We present MicroBotNet, a neural network architecture for image classification with fewer than 1 million multiply-and-accumulate (MAC) operations. We wish to explore architectures suitable for microrobots with a goal of less than 1 µJ per forward-pass. We estimate this to be feasible with 1 million MAC operations. MicroBotNet achieves 80.47% accuracy with 740,000 MAC operations on CIFAR-10. Additionally, 60% of weights are quantized to {1, 0, +1} using Trained Ternary Quantization. We also evaluate MicroBotNet on our Micro Robot Dataset, which is composed of 10 image classes a microrobot may encounter such as acorns, mushrooms, and ladybugs. After applying transfer learning, MicroBotNet achieves 67.80% accuracy on the Micro Robot Dataset. Finally, we test MicroBotNet on acorn images simulating a microrobot. MicroBotNet correctly identify the acorn in 7 out of 8 cases when approaching an acorn and 15 out of 16 cases from different angles using a best of last three frames filter.