Spiking Neural Network (SNN) is of special interest among the various NN algorithms. While the SNN has been trained before using the biologically plausible Spike Time Dependent Plasticity (STDP) rule on conventional computers and specialized digital neuromorphic chips, analog hardware is more suitable for such temporal-domain-based training. Further, complete memory-computing intertwining can be achieved with analog hardware only, thereby removing the von Neumann bottleneck altogether. Hence, in this paper, we have designed an analog hardware SNN with neuron and synapse blocks, which are based on conventional silicon transistors and capacitors. Our synapse-block exhibits STDP rule for weight-update. Our neuron-block follows the biologically plausible Leaky Integrate Fire (LIF) model with mechanism for spike generation and homoeostasis property. Then we have shown on-chip learning on a popular Machine Learning (ML) data-set (Fisher’s Iris) through SPICE simulations of the complete hardware. Previous reports on analog hardware SNN mostly involve the use of emerging devices, which are much harder to fabricate compared to conventional silicon transistors that are used here. Those reports also do not show iterative learning through full circuit-level design and simulation of the SNN, unlike this paper. Moreover, other existing reports of analog SNN, which use conventional transistors, do not show training on popularly used ML data-sets, unlike this paper.